Tutorial #2 - Capacity Constraint and Iterations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast-Trips Tutorial 2: Capacity Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os,datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specify Input Networks and Demand\n",
    "To start with, we will use the same networks as previous scenarios, but we will be using an expanded demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "##REPLACE THIS WITH THE PLACE YOUR TTA FOLDER IS\n",
    "BASE_DIR         = r\"YOUR PATH TO THE TUTORIAL DIRECTORY\"\n",
    "\n",
    "INPUT_NETWORKS   = os.path.join(BASE_DIR,\"tta\",\"input\",\"network-simple\")\n",
    "INPUT_DEMAND     = os.path.join(BASE_DIR,\"tta\",\"input\",\"demand-bigger\")\n",
    "\n",
    "RUN_CONFIG       = os.path.join(BASE_DIR,\"tta\", \"input\",\"demand-bigger\",\"config_ft.txt\")\n",
    "INPUT_WEIGHTS    = os.path.join(BASE_DIR,\"tta\", \"input\",\"demand-bigger\",\"pathweight_ft.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Fast-Trips \n",
    "This example runs fast-trips for a much larger amount of demand.  It looks like the game is over and all the basketball players want to go to the park!\n",
    "\n",
    "Note that each bus only has a capacity of 10 and more than 10 riders will want to ride any given bus at once.  Therefore, we will run several iterations so that they will have a chance to find an alternate route.\n",
    "\n",
    "Also note that this run will take a few minutes to complete since it is running more people and multiple iterations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>person_trip_id</th>\n",
       "      <th>o_taz</th>\n",
       "      <th>d_taz</th>\n",
       "      <th>mode</th>\n",
       "      <th>purpose</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>time_target</th>\n",
       "      <th>vot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tuffie</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rameses</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry Stackhouse</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dean Smith</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vince Carter</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antawn Jamison</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Serge Zwikker</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dante Calabria</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shammond Williams</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Marcus Melvin</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Archie Miller</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Julius Hodge</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Illian Evtimov</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Scooter Sherrill</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Josh Powell</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Anthony Grundy</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:00:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mike K.</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jeff Capel</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Grant Hill</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bobby Hurley</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Christian Laettner</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Jayson Williams</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Shane Battier</td>\n",
       "      <td>1</td>\n",
       "      <td>O</td>\n",
       "      <td>D</td>\n",
       "      <td>transit</td>\n",
       "      <td>basketball</td>\n",
       "      <td>7:05:00</td>\n",
       "      <td>8:00:00</td>\n",
       "      <td>departure</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             person_id  person_trip_id o_taz d_taz     mode     purpose  \\\n",
       "0               Tuffie               1     O     D  transit  basketball   \n",
       "1              Rameses               1     O     D  transit  basketball   \n",
       "2       Michael Jordan               1     O     D  transit  basketball   \n",
       "3     Jerry Stackhouse               1     O     D  transit  basketball   \n",
       "4           Dean Smith               1     O     D  transit  basketball   \n",
       "5         Vince Carter               1     O     D  transit  basketball   \n",
       "6       Antawn Jamison               1     O     D  transit  basketball   \n",
       "7        Serge Zwikker               1     O     D  transit  basketball   \n",
       "8       Dante Calabria               1     O     D  transit  basketball   \n",
       "9    Shammond Williams               1     O     D  transit  basketball   \n",
       "10            Mrs. Wuf               1     O     D  transit  basketball   \n",
       "11       Marcus Melvin               1     O     D  transit  basketball   \n",
       "12       Archie Miller               1     O     D  transit  basketball   \n",
       "13        Julius Hodge               1     O     D  transit  basketball   \n",
       "14      Illian Evtimov               1     O     D  transit  basketball   \n",
       "15    Scooter Sherrill               1     O     D  transit  basketball   \n",
       "16         Josh Powell               1     O     D  transit  basketball   \n",
       "17      Anthony Grundy               1     O     D  transit  basketball   \n",
       "18             Mike K.               1     O     D  transit  basketball   \n",
       "19          Jeff Capel               1     O     D  transit  basketball   \n",
       "20          Grant Hill               1     O     D  transit  basketball   \n",
       "21        Bobby Hurley               1     O     D  transit  basketball   \n",
       "22  Christian Laettner               1     O     D  transit  basketball   \n",
       "23       Carlos Boozer               1     O     D  transit  basketball   \n",
       "24     Jayson Williams               1     O     D  transit  basketball   \n",
       "25       Shane Battier               1     O     D  transit  basketball   \n",
       "\n",
       "   departure_time arrival_time time_target  vot  \n",
       "0         7:00:00      8:00:00   departure   10  \n",
       "1         7:00:00      8:00:00   departure   10  \n",
       "2         7:00:00      8:00:00   departure   10  \n",
       "3         7:00:00      8:00:00   departure   10  \n",
       "4         7:00:00      8:00:00   departure   10  \n",
       "5         7:00:00      8:00:00   departure   10  \n",
       "6         7:00:00      8:00:00   departure   10  \n",
       "7         7:00:00      8:00:00   departure   10  \n",
       "8         7:00:00      8:00:00   departure   10  \n",
       "9         7:00:00      8:00:00   departure   10  \n",
       "10        7:00:00      8:00:00   departure   10  \n",
       "11        7:00:00      8:00:00   departure   10  \n",
       "12        7:00:00      8:00:00   departure   10  \n",
       "13        7:00:00      8:00:00   departure   10  \n",
       "14        7:00:00      8:00:00   departure   10  \n",
       "15        7:00:00      8:00:00   departure   10  \n",
       "16        7:00:00      8:00:00   departure   10  \n",
       "17        7:00:00      8:00:00   departure   10  \n",
       "18        7:05:00      8:00:00   departure   10  \n",
       "19        7:05:00      8:00:00   departure   10  \n",
       "20        7:05:00      8:00:00   departure   10  \n",
       "21        7:05:00      8:00:00   departure   10  \n",
       "22        7:05:00      8:00:00   departure   10  \n",
       "23        7:05:00      8:00:00   departure   10  \n",
       "24        7:05:00      8:00:00   departure   10  \n",
       "25        7:05:00      8:00:00   departure   10  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand_df = pd.read_csv(os.path.join(INPUT_DEMAND,\"trip_list.txt\"))\n",
    "demand_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fasttrips import Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pathweights_df = pd.read_csv(INPUT_WEIGHTS)\n",
    "pathweights_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Fast-Trips with Capacity Constraint\n",
    "\n",
    "Note the following arguments:\n",
    "\n",
    "  * `capacity = True ` \n",
    "  * `iters    = 3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "OUTPUT_DIR   = os.path.join(BASE_DIR,\"tta\",\"output\")\n",
    "OUTPUT_FOLDER = \"capacity-demand\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-06-15 16:52:41 [INFO/MainProcess] Reading configuration file /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/input/demand-bigger/config_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Reading GTFS schedule\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       6           stops from                 stops.txt,              stops_ft.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dispersion': 0.5, 'overlap_variable': 'count', 'capacity': True, 'output_pathset_per_sim_iter': True}\n",
      "routes_ft.txt\n",
      "The file named routes_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "transfers_ft.txt\n",
      "The file named transfers_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "trips_ft.txt\n",
      "The file named trips_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "vehicles_ft.txt\n",
      "The file named vehicles_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "walk_access_ft.txt\n",
      "The file named walk_access_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       3          routes from                routes.txt,             routes_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       1        agencies from                agency.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       0 fare attributes from       fare_attributes.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       0      fare rules from            fare_rules.txt,       fare_periods_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       2       transfers from             transfers.txt,          transfers_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       1        vehicles from           vehicles_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       9           trips from                 trips.txt,              trips_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       1 service periods from              calendar.txt\n",
      "2017-06-15 16:52:41 [WARNING/MainProcess] Adding shape_dist_traveled in miles\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read      18      stop times from            stop_times.txt,         stop_times_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       3     walk access from        walk_access_ft.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       0            DAPs from drive_access_points_ft.txt\n",
      "2017-06-15 16:52:41 [WARNING/MainProcess] The following 3 stop ids have no walk access: \n",
      "  stop_id  taz\n",
      "1       B  NaN\n",
      "2      B1  NaN\n",
      "3      B2  NaN\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] -------- Reading demand --------\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Capacity constraint? 1\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read      26    person trips from             trip_list.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read      26         persons from                person.txt\n",
      "2017-06-15 16:52:41 [INFO/MainProcess] Read       3      households from             household.txt\n",
      "2017-06-15 16:52:42 [INFO/MainProcess] Have 26 person trips\n",
      "2017-06-15 16:52:42 [INFO/MainProcess] Wrote veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:42 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 1 **************************************\n",
      "2017-06-15 16:52:42 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:52:42 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "/Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/working/fast-trips/fasttrips/Assignment.py:833: FutureWarning: to_datetime is deprecated. Use self.to_pydatetime()\n",
      "  trip_pathset = PathSet(path_dict)\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:42 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] Wrote pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] Wrote pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] Wrote performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-06-15 16:52:43 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:43 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:52:44 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:52:44 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:52:44 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]           Need to bump 10 passengers from 2 trip-stops\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 8 chosen paths\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:52:45 [INFO/MainProcess] Wrote pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:52:45 [INFO/MainProcess] Wrote pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:52:45 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:45 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:45 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:46 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:46 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           Rejecting 8 previously chosen paths for huge costs\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           No choosable paths\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:52:47 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:52:47 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:52:47 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:47 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Wrote chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Wrote chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] \n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   ARRIVED PASSENGERS:                18\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   MISSED PASSENGERS:                  8\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   CAPACITY GAP:                30.76923\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 2 **************************************\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Finding pathsets for 8 trips\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       1 paths sought,      1 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       2 paths sought,      2 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       3 paths sought,      3 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       4 paths sought,      4 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       5 paths sought,      5 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       6 paths sought,      6 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       7 paths sought,      7 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]       8 paths sought,      8 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Finished finding      8 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-06-15 16:52:48 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:48 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:49 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:49 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:52:49 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]           Chose 8 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]           Need to bump 12 passengers from 2 trip-stops\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 6 chosen paths\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:50 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:52:51 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:52:51 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:52:51 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:51 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:51 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:52:52 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]           Rejecting 6 previously chosen paths for huge costs\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]           Have     26 total passenger-trips, with     20 chosen paths,      6 fully rejected and      0 needing a choice\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:53 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:52:53 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] \n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   MISSED PASSENGERS:                  6\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   CAPACITY GAP:                23.07692\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 3 **************************************\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Finding pathsets for 6 trips\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       1 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       2 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       3 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       4 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       5 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]       6 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Finished finding      0 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] \n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   Number of pathsets found:          20\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-06-15 16:52:54 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 1 **************************************\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:52:54 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 1s\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-06-15 16:52:55 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:55 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:56 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:56 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:56 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:52:57 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:52:57 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]           Need to bump 13 passengers from 2 trip-stops\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 8 chosen paths\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:52:58 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:52:59 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:52:59 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:52:59 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:52:59 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:52:59 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:00 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]           Rejecting 8 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]           No choosable paths\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] \n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   ARRIVED PASSENGERS:                18\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   MISSED PASSENGERS:                  8\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]   CAPACITY GAP:                30.76923\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 2 **************************************\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Finding pathsets for 8 trips\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       1 paths sought,      1 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       2 paths sought,      2 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       3 paths sought,      3 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       4 paths sought,      4 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       5 paths sought,      5 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       6 paths sought,      6 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       7 paths sought,      7 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess]       8 paths sought,      8 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:01 [INFO/MainProcess] Finished finding      8 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:02 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:53:02 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:53:02 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:53:02 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-06-15 16:53:02 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:53:02 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:02 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:02 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:02 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:02 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:03 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           Chose 8 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           Need to bump 12 passengers from 2 trip-stops\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 6 chosen paths\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:04 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:04 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:05 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:05 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:05 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:53:05 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:05 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:05 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:05 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:05 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]           Rejecting 6 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]           Have     26 total passenger-trips, with     20 chosen paths,      6 fully rejected and      0 needing a choice\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] \n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   MISSED PASSENGERS:                  6\n",
      "2017-06-15 16:53:06 [INFO/MainProcess]   CAPACITY GAP:                23.07692\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 3 **************************************\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:53:06 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] Finding pathsets for 6 trips\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       1 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       2 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       3 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       4 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       5 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]       6 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] Finished finding      0 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] \n",
      "2017-06-15 16:53:07 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]   Number of pathsets found:          20\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-06-15 16:53:07 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 1 **************************************\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:53:07 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-06-15 16:53:08 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:08 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:09 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]           Need to bump 10 passengers from 2 trip-stops\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 8 chosen paths\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:10 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:10 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:10 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:10 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:10 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:11 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:11 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:11 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           Rejecting 8 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           No choosable paths\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:12 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:53:12 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] \n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   ARRIVED PASSENGERS:                18\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   MISSED PASSENGERS:                  8\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   CAPACITY GAP:                30.76923\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 2 **************************************\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Finding pathsets for 8 trips\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       1 paths sought,      1 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       2 paths sought,      2 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       3 paths sought,      3 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       4 paths sought,      4 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       5 paths sought,      5 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       6 paths sought,      6 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       7 paths sought,      7 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]       8 paths sought,      8 paths found of 8 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Finished finding      8 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-06-15 16:53:13 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:13 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:14 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           Have     26 total passenger-trips, with     18 chosen paths,      0 fully rejected and      8 needing a choice\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           Chose 8 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           Need to bump 12 passengers from 2 trip-stops\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 6 chosen paths\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:15 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:15 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:15 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:15 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-06-15 16:53:15 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-06-15 16:53:16 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-06-15 16:53:16 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-06-15 16:53:16 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    310);  mem_use=Uknown; please install python package psutil\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]           Rejecting 6 previously chosen paths for huge costs\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]           Have     26 total passenger-trips, with     20 chosen paths,      6 fully rejected and      0 needing a choice\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-06-15 16:53:17 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_paths.csv\n",
      "2017-06-15 16:53:17 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathset_links.csv\n",
      "2017-06-15 16:53:17 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:17 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-06-15 16:53:17 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_links.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/chosenpaths_paths.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] \n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   MISSED PASSENGERS:                  6\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   CAPACITY GAP:                23.07692\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 3 **************************************\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Finding pathsets for 6 trips\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       1 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       2 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       3 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       4 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       5 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]       6 paths sought,      0 paths found of 6 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Finished finding      0 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_paths.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/pathsfound_links.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/ft_output_performance.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand/veh_trips.csv\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] \n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   Number of pathsets found:          20\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   ARRIVED PASSENGERS:                20\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-06-15 16:53:18 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-06-15 16:53:18 [INFO/MainProcess] Successfully completed!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'capacity_gap': 0.0,\n",
       " 'passengers_arrived': 20,\n",
       " 'passengers_demand': 26,\n",
       " 'passengers_missed': 0,\n",
       " 'paths_found': 20}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Run.run_fasttrips(input_network_dir= INPUT_NETWORKS,\n",
    "                  input_demand_dir = INPUT_DEMAND,\n",
    "                  run_config       = RUN_CONFIG,\n",
    "                  input_weights    = INPUT_WEIGHTS,\n",
    "                  output_dir       = OUTPUT_DIR,\n",
    "                  output_folder    = OUTPUT_FOLDER,\n",
    "                  iters            = 3,\n",
    "                  overlap_variable = \"count\",\n",
    "                  dispersion       = 0.5,\n",
    "                  capacity         = True,\n",
    "                  output_pathset_per_sim_iter = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uhoh - it looks like some of your passengers didn't make it!  Let's figure out what is going on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vehicle/Route Level\n",
    "First, let's examine what is going on with the vehicles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iteration</th>\n",
       "      <th>pathfinding_iteration</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>route_id</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>stop_id</th>\n",
       "      <th>boards</th>\n",
       "      <th>onboard</th>\n",
       "      <th>standees</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:25:41</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:19:41</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:17:53</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:09:41</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:19:41</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:17:53</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
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       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
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       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:19:41</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
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       "      <td>G3</td>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:17:53</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
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       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1900-01-01 07:26:00</td>\n",
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       "      <td>B2</td>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
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       "      <td>9</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:17:55</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:09:41</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:19:39</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:17:55</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:19:39</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:17:55</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:25:41</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:19:45</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:17:49</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-01 07:09:41</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:19:45</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:17:49</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:25:37</td>\n",
       "      <td>1900-01-01 07:26:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G4</td>\n",
       "      <td>B2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:19:45</td>\n",
       "      <td>1900-01-01 07:20:00</td>\n",
       "      <td>Green</td>\n",
       "      <td>G3</td>\n",
       "      <td>B2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:17:49</td>\n",
       "      <td>1900-01-01 07:18:00</td>\n",
       "      <td>Red</td>\n",
       "      <td>R2</td>\n",
       "      <td>B1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:03:37</td>\n",
       "      <td>1900-01-01 07:04:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B2</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1900-01-01 07:09:37</td>\n",
       "      <td>1900-01-01 07:10:00</td>\n",
       "      <td>Blue</td>\n",
       "      <td>B3</td>\n",
       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     iteration  pathfinding_iteration        arrival_time      departure_time route_id trip_id stop_id  boards  onboard  standees\n",
       "54           1                      1 1900-01-01 07:25:41 1900-01-01 07:26:00    Green      G4      B2       8        8         3\n",
       "56           1                      1 1900-01-01 07:19:41 1900-01-01 07:20:00    Green      G3      B2       8        8         3\n",
       "62           1                      1 1900-01-01 07:17:53 1900-01-01 07:18:00      Red      R2      B1       2        2         0\n",
       "66           1                      1 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "68           1                      1 1900-01-01 07:09:41 1900-01-01 07:10:00     Blue      B3       A       8        8         3\n",
       "108          1                      2 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "110          1                      2 1900-01-01 07:19:41 1900-01-01 07:20:00    Green      G3      B2       8        8         3\n",
       "116          1                      2 1900-01-01 07:17:53 1900-01-01 07:18:00      Red      R2      B1       2        2         0\n",
       "120          1                      2 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "122          1                      2 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5\n",
       "126          1                      3 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "128          1                      3 1900-01-01 07:19:41 1900-01-01 07:20:00    Green      G3      B2       8        8         3\n",
       "134          1                      3 1900-01-01 07:17:53 1900-01-01 07:18:00      Red      R2      B1       2        2         0\n",
       "138          1                      3 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "140          1                      3 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5\n",
       "180          2                      1 1900-01-01 07:25:41 1900-01-01 07:26:00    Green      G4      B2       8        8         3\n",
       "182          2                      1 1900-01-01 07:19:39 1900-01-01 07:20:00    Green      G3      B2       9        9         4\n",
       "188          2                      1 1900-01-01 07:17:55 1900-01-01 07:18:00      Red      R2      B1       1        1         0\n",
       "192          2                      1 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "194          2                      1 1900-01-01 07:09:41 1900-01-01 07:10:00     Blue      B3       A       8        8         3\n",
       "234          2                      2 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "236          2                      2 1900-01-01 07:19:39 1900-01-01 07:20:00    Green      G3      B2       9        9         4\n",
       "242          2                      2 1900-01-01 07:17:55 1900-01-01 07:18:00      Red      R2      B1       1        1         0\n",
       "246          2                      2 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "248          2                      2 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5\n",
       "252          2                      3 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "254          2                      3 1900-01-01 07:19:39 1900-01-01 07:20:00    Green      G3      B2       9        9         4\n",
       "260          2                      3 1900-01-01 07:17:55 1900-01-01 07:18:00      Red      R2      B1       1        1         0\n",
       "264          2                      3 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "266          2                      3 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5\n",
       "306          3                      1 1900-01-01 07:25:41 1900-01-01 07:26:00    Green      G4      B2       8        8         3\n",
       "308          3                      1 1900-01-01 07:19:45 1900-01-01 07:20:00    Green      G3      B2       6        6         1\n",
       "314          3                      1 1900-01-01 07:17:49 1900-01-01 07:18:00      Red      R2      B1       4        4         0\n",
       "318          3                      1 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "320          3                      1 1900-01-01 07:09:41 1900-01-01 07:10:00     Blue      B3       A       8        8         3\n",
       "360          3                      2 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "362          3                      2 1900-01-01 07:19:45 1900-01-01 07:20:00    Green      G3      B2       6        6         1\n",
       "368          3                      2 1900-01-01 07:17:49 1900-01-01 07:18:00      Red      R2      B1       4        4         0\n",
       "372          3                      2 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "374          3                      2 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5\n",
       "378          3                      3 1900-01-01 07:25:37 1900-01-01 07:26:00    Green      G4      B2      10       10         5\n",
       "380          3                      3 1900-01-01 07:19:45 1900-01-01 07:20:00    Green      G3      B2       6        6         1\n",
       "386          3                      3 1900-01-01 07:17:49 1900-01-01 07:18:00      Red      R2      B1       4        4         0\n",
       "390          3                      3 1900-01-01 07:03:37 1900-01-01 07:04:00     Blue      B2       A      10       10         5\n",
       "392          3                      3 1900-01-01 07:09:37 1900-01-01 07:10:00     Blue      B3       A      10       10         5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth',160) \n",
    "\n",
    "vehicles_df = pd.read_csv(os.path.join(OUTPUT_DIR,OUTPUT_FOLDER,\"veh_trips.csv\"), \n",
    "                                       sep=\",\", \n",
    "                                       parse_dates=['arrival_time', 'departure_time'],\n",
    "                                       date_parser=lambda x: datetime.datetime.strptime(x, '%H:%M:%S') )\n",
    "# dump intermediate simulation_iteration results\n",
    "vehicles_df = vehicles_df.loc[vehicles_df[\"simulation_iteration\"]==\"final\"]\n",
    "\n",
    "vehicles_with_boardings_df=vehicles_df[vehicles_df[\"onboard\"]>0]\n",
    "vehicles_with_boardings_df[['iteration','pathfinding_iteration','arrival_time','departure_time','route_id','trip_id','stop_id','boards','onboard','standees']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/envs/fast-trips-develop/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11dffdb50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OAAAAAJwQYQ4AAAAAnBBhDgDQ4OXl5envf/97ue0xMTGaP39+jdqc\nP3++YmJiym1fuHChFi5cWKM2AQC4FDxnDgDQ4OXk5Ojw4cPltvfr10/9+vWrUZvjxo273LIAALgs\nhDkAQIM3a9YspaamasyYMTp+/LgCAwPl6empe++9V7t379Yrr7yivn37qm/fvtq7d6+kCw9M7tSp\nk802J0+erBtvvFH333+/3nvvPa1fv16BgYHy8/NTly5d7PXRAABXMKZZAgAavKlTpyo0NFTPP/+8\nfvvtN82ZM0fLli0rt19AQIA++eQTPfPMM5o0aVK12v7pp5/08ccfa+PGjVq6dKlOnz5dy9UDAFAx\nRuYAADWybNkyJSQk6LbbblOfPn3KvZ+enq6QkBD7F1aF4OBgtWzZUpJUWFhY5r2hQ4daP5eXl5cy\nMzMVFBRUaXu7d+/WbbfdJh8fH0nSgAEDZLFY6qb4/3P+/Hm9+uqruvrqq/XII49Yt3/66af68ccf\nNXbsWAUHB1u3WywW7du3TwcOHFBqaqrMZrMaN26sdu3a6dZbb1VAQECd1gsAqBuEOQBArcrIyNB/\n/vMfeXl5aejQoY4upxwvLy+VlJRo+/bt+vXXX8u85+b2vx+LhmHI1dW1yvZMJlOZ8Obm5qaioqLa\nK7gCKSkpkqTmzZuX2Z6cnCxPT88yAdRsNmvNmjX67bffJEmNGzeWv7+/MjIyFBcXp4MHD+qRRx6x\nBlwAgPNgmiUAoFb99NNP1uBQX7i5uclsNltf5+XlaceOHTIMo8x+//nPfzR48GBdf/31cnd3l7+/\nf5Vt9+jRQ998843y8vJUWFior776qtbr/6Pk5GRJZcNccXGx0tLS1KxZM5lMJuv2bdu26bffflPj\nxo01cuRIjR8/XqNHj9aECRPUoUMHnT9/XuvXr1dxcXGd1w0AqF2MzAEAGrzg4GA1b95czz//fKX7\n7du3Tx999JG8vb01e/bsarXdsWNHPfbYY3rwwQfl5+dXbrSsLlQU5k6fPi3DMMpsM5vN1gVd7r77\nbl111VXW97y9vXX//fdr0aJFysnJ0cGDB3X99dfXee0AgNpDmAMANHju7u5au3at9XV2drb1zy++\n+KL1zxMmTKj2dMNXXnnF+udHHnmkzL1rdS05OVm+vr5q3LhxmW1S2YCXnJysoqIiubq66k9/+lO5\ndtzd3dWmTRv9+OOPSklJIcwBgJMhzAEAas0LL7xg/fOhQ4f0wgsv6KqrrtLIkSOt2wsLC7Vr1y4d\nOnRImZmZcnFxUWhoqK6//np17dpVLi5l7wB48803lZOTo6efflpbt27V0aNH5erqqvbt22vw4MHW\nNvfs2aOjR48qPT1dhYWF8vDwUFhYmLp06aKuXbtapx5+8sknio+Pt7ZfOgLn6ekp6X8Lu9x1113a\ntm2bdu7cad3X29tbYWFhCgwMlKurq7y8vNSyZUvddNNNatu2bZm6s7OzNX/+fDVp0kSjRo3Srl27\nFB8fr6ysLLm7u6tVq1bq3bt3tUbyvvnmG23fvr3S813qo48+0kcffaTHHntMoaGhGjp0qAoLC8tM\nvbxY6f19db1oCwCg9hHmAAC1Jjw8XDk5OcrNzZW3t7dCQkIUGhpqfT87O1srV660hrjg4GAZhqHE\nxEQlJibq8OHDeuihh8osRFJq48aNSkpKUlhYmHJzc633s+Xk5Gj58uXKysqSq6urgoKC5O/vr6ys\nLCUkJCghIUHJyckaOHCgpP9NuSwdyWrZsqVMJpMmTJggX1/fMn1e/HiCPXv26Msvv5RhGPLy8lJQ\nUJByc3N19OhRHT16VD179tQdd9xRrm6LxaI1a9bo+PHj8vHxUUhIiNLS0nTkyBEdO3ZMI0eOrHI0\n0N/fX+Hh4ZKks2fPKiMjQ4GBgWXqTU5OlslkUrNmzSRdCKeNGjVSx44dbbZbWFiokydPSpKaNGlS\naQ0AgPqHMAcAqDVPPPGEdRSpdevWZVaztFgsWrdunTIzM/WnP/1JAwcOtIaRtLQ0rV+/XseOHdPX\nX3+tAQMGlGs7JSVFjz/+uMLDw1VSUmJd0GTLli3KysrSVVddpSFDhlgfEWA2m7V161bt2rVLcXFx\n6tOnj3x9fdWrVy9de+21mj9/viQpKipKHh4elX6ukydPatOmTZKkvn376pZbbpGLi4sMw9CBAwf0\n+eefa+fOnQoODla3bt3KHJuRkaHc3Fw98MAD6ty5s6QLC7CsXLlSaWlp2r59e5VTNLt27aquXbtK\nknbu3KmvvvpKAwYMUPv27SVdWPxk9uzZatOmjaKioipt62I7duzQ2bNn5ebmVmnoAwDUT6xmCQCw\ni8OHD+v06dMKDg7Wgw8+WGZUqUmTJhoyZIhMJpP27t2rgoKCcsd37NjROjrl6uoqT09Pmc1mJSYm\nSpIGDhxoDXLShRUs+/fvb328QHp6eo1r//bbbyVJERER6tWrl3UqqMlk0nXXXaf+/ftLujAdsqLp\nir1797YGOenC4wFuvfVWSbLWX12lDyUvHYGTpDNnzsgwDDVt2rTa7Rw8eFDff/+9JKlnz55l7r8D\nADgHwhwAwC6OHDkiSerQoUOF0yhDQ0MVGhqqkpKSCh9tUNFURDc3N40fP15Tpkyp8AHlZrNZ3t7e\nklTjpfeLioqUkJAgSbrhhhsq3CciIkKurq7Ky8uzPgPuYtdcc025baUP9f7jg8urkpKSIh8fnyoX\nP6nMoUOHtGHDBklS69atddttt11SDQCA+oFplgAAu0hLS5Mk/fLLL/r9998r3Cc3N1dSxaNolY0c\nubu7Kzs7W6dOnVJGRoays7OVlpamM2fOqKSkRJLKPVOuurKysmSxWKwLtdjqPyQkRGfOnFFGRoZa\ntGhR5n0/P78Kj7nUuoqKipSRkaF27dqV2V4aIC8erbNl3759+uKLL6yPMXjooYfKLToDAHAOhDkA\ngF2UjkBlZWUpKyurWvterKLRPOnCAiibN2/W4cOHy2z39fVVp06d9Ouvv+r8+fM1rPp/tXh4eNhc\nEbL0fel/q0NerHSqZ03t379f+/fvl9lslmEYOn36tN5//33r+6mpqZIurNRZ6q677ioX7i5eFTM8\nPFzDhw+Xl5fXZdUGAHAcwhwAwC5KR6KGDBmiTp061UqbxcXFWrFihTIzMxUQEKDu3burefPmatKk\nifWevHnz5l1WHxeHNMMwbAa6i0NfbcvJydGpU6esr/Pz85Wfn19uv4v3uTgQG4ahL7/8Unv27JEk\nXX311Ro6dKj17wQA4JwIcwAAuwgODtaZM2cqXYjk1KlT8vT0VGBgYLWCxuHDh5WZmSlvb2/97W9/\nU6NGjcq8bzabde7cucuqOygoSCaTSRaLRampqQoLCyu3T1FRkfVzBQUFXVZ/FenTp4/69OmjjRs3\n6sCBA4qOjrY+miEpKUnvvfeebr75ZkVGRlZ4/JYtW6xBrkuXLho0aBBTKwGgAeCbHABQq2yNXJUu\nAhIfH299rMDFsrKytGzZMi1evLjMCFNlsrOzJV14Dtsfg1xpX6X3zF28yuTFNVZ1z5qHh4dat24t\nSdZA9EdxcXGyWCzy9vau1n1rNZWcnCwfHx9rkCvdJtle/GTfvn2KjY2VdGGhlvvuu48gBwANBN/m\nAIBaVTrNMCcnp8z2zp07KygoSJmZmVq/fn2ZaYIZGRlau3atLBaLmjZtqrZt21arr9IVIc+cOWNd\nLVOSSkpKtG/fPm3ZssW67eIAefFUyD/WWZHevXvLZDIpLi5O3333nTUYGoah+Ph4xcTESJJuv/32\ny74/zpbSxU/+GBZLFz+pKMydPXvWeg7atm2rv/zlL5Xe9wcAcC5MswQA1KrSaYjJyclauHChwsLC\nNHToULm5uWnYsGFatWqVfv31V73xxhtq0qSJSkpKlJGRIcMw5Ofnp2HDhlW7rw4dOqhZs2ZKSUnR\n2rVrFRgYKC8vL2VlZen8+fPy9vZWUFCQzpw5o7y8POtx3t7e8vPzU25urpYuXaqgoCANGjTI5mqV\nrVu31oABA7R582bFxMRo586dCgoKUk5OjjWU3nTTTTYfXVAbUlJSrCtQ/nG7p6dnhdM79+3bZ12Q\nJS8vT0uXLrXZ/jXXXKNevXrVbtEAgDpFmAMA1Kq2bduqb9++2rt3r3JycmSxWKwLhzRp0kSjRo3S\nrl27dOTIEWuICwoKUvv27XXLLbeUefB3VVxcXDRy5Eh9//33OnTokLKzs5Wfny9/f3917dpVPXr0\n0JEjR/Sf//xHR48eVY8ePazHDhkyRF9++aVSU1OVmZmprKwsm2FOkm688Ua1aNFCP/zwg06ePGl9\n3lvHjh11ww03qE2bNpd13qpS0XRKs9ms1NRUtWrVqsIRt4sfAVH6aAhb6uJePwBA3TIZNX3wDgAA\nAADAYbhnDgAAAACcEGEOAAAAAJwQYQ4AAAAAnBBhDgAAAACcUL1ezTIuLs7RJQAAAACAQ0VERFS4\nvV6HOcl24QAAAADQ0FU2wMU0SwAAAABwQoQ5AAAAAHBChDkAAAAAcEKEOQAAAABwQoQ5AAAAAHBC\nhDkAAAAAcEKEOQAAAABwQnUa5uLj4xUVFSVJSkhI0MMPP6zhw4dr+vTpslgsddk1AAAAADRodRbm\n3n33XU2dOlWFhYWSpNmzZys6Olpr1qyRYRiKiYmpq65t2rx5s3JzcxUbG6tp06aVe3/58uUaPHiw\n9u7dq6VLl1arzaioKKWlpWnJkiU6duzYZdf43HPPSZJiY2OVkJBQ43aSkpL0/fffl2kTAAAAQMNR\nZ2GuVatWWrhwofX1wYMHdeONN0qSevfurZ07d9ZV1zatXr3aGi4rEhMTo8WLF6t79+56/PHHL6nt\np556SldfffXllqjXXntNkrRx40ZlZWXVuJ3Y2FgdPHiwTJsAAAAAGg63umo4MjJSiYmJ1teGYchk\nMkmSfHx8lJeXV6124uLiqtxn+/bt2rt3r86ePavCwkKNHj1aMTExSkpKUm5uru68806FhITo559/\n1pgxY3TPPfcoLi5ODz74oHJzczV8+HDl5ubqp59+0pNPPqnBgwcrLi5O9957rxYvXqzGjRsrJSVF\nd999t/r06aPvvvtOmzZtUlBQkFJSUnTgwAGtXbtW/fr1U3x8vFJTU5WTk6OCggJFR0fL19dXCxcu\n1Pnz5xUcHCwXFxeNGjWqws8yYcIEjRs3Tlu3btX+/fs1Y8YMbd68Wfv27ZNhGHrggQfUpUsXTZky\nRQEBAWrVqpU6deqkzz77TBaLRd7e3powYYL+/e9/q6ioSI0bN9Y777yjefPmaf/+/dqwYYNcXV3V\nrl07RUVF6aOPPipXb0hISJXnfN66w9X6+7scHh1312n7Q7fl1mn7khQ2bmyd91GbFh5YUaftc84B\nAEBt4feWOgxzf+Ti8r9BwIKCAvn5+VXruIiIiCr3SUhI0MmTJ7VgwQLt3btXCxcuVGRkpIYPH660\ntDSNGjVKH3/8sWJiYvT666/rxIkT8vHx0QcffKCff/5ZS5Ys0aJFi7Rnzx7r+wkJCercubPy8vL0\n4YcfKisrS0899ZQmTJigadOm6dNPP5Uk3XnnnerSpYu++uordejQQWlpaWrSpInGjx+vt99+Wykp\nKTIMQ/3799eTTz6pDRs2aPfu3TY/l5eXlwYPHqzY2FgNGzZM3t7eSklJ0WeffaazZ89q+PDhGjly\npIqLizV37lyFh4dr1apVWrlypXx9ffXkk08qICBAY8eOVXp6uh5++GEtX75c3bp109SpU7Vu3Tr5\n+fnp2WefVUFBgZo3b16u3sjIyKr/YuwQ5hqC6ly/9Uodfynag9OdcwAAUDNXyO8tlQ1u2S3MderU\nSbGxsbrpppv07bff6uabb67V9kuncF577bVKTU3ViRMnNHHiRPn4+MhsNpfbv0OHDnJ1dVVwcLDO\nnz9vs902bdrIw8NDYWFhKiwsVHp6uoKCguTl5SVJat++fbljSreFhoYqPT1diYmJGjhwoCTpuuuu\n0+7d1R9xOn78uI4dO6YRI0ZIkgoLC5WVlSV3d3eFh4dLkoKCgvTPf/5TjRo1UkpKSoWfNzMzUyEh\nIdYQ3a1bN504caLCegEAAADUf3Z7NMGkSZO0cOFCPfTQQyouLq7e6M8l+OWXXyRJBw4c0KlTp2QY\nhubMmaOBAwfKMAzrfpe7imZAQIDS0tJUUFCgoqIiHT9+vNw+pdNJS7Vr104//fSTJFn/vyomk0kW\ni0WtW7fW9ddfr5UrV+r999/X3XffLX9//zJ9zJ07V/PmzdOMGTNkMpmsU1ov/qyBgYFKS0tTfn6+\npAsJvzQM/rFeAAAAAPVfnY7MtWzZUuvXr5d0YYRr1apVddbXiRMnNGLECJWUlGjFihWaOXOmhg37\n/+zdd3RUdcLG8WfSQwJpQBAIUpQFZUWMqGChukFfkaIgIgFE11eWFkWkLAJSpKkIHIrgoSOIr8Cq\n64ICEdlFIkaKroACEgkJkF4MpN73DzezRjKZEDLlhu/nHM9m7sz8fk/uXpM8/u7cO0DBwcEyDEMl\nJSW6/fbbNWbMGMXExFR5Hi8vL73wwgsaNGiQ6tatq5CQELvv6devn1566SXt3btXtWvXVnBwsN33\ntGnTRtOnT9eqVavUokULDRw4UHl5eXrsscfk6elZ5rWdO3dW3759VatWLWtpu/nmm7VixQq1bdtW\n0q+nuY4fP17Dhg2TYRhq06aNunbtai3BAAAAAMzFYvx22crNxMfHV+o80q1btyo1NVXPPfecE1Jd\nvfj4eBmGoTvvvFPbt2/X2bNnNWrUKFfHuiYDX97o8DlqwgVQHl5XuVtcuIuhq8c4dHz2OQAAqC7X\ny98tFXUip31m7nrWqFEjxcTEyGKxyN/fX/PmzdPo0aOvuPVAVFSUBg0a5KKUAAAAAMykRpS5vn37\nujpChRo0aKDNmzeX2bZo0SIXpQEAAABQEzjtAigAAAAAgOpDmQMAAAAAE6LMAQAAAIAJ1YjPzDlD\ndV+98d15T1XreAAAAACuL6zMubkjR44oOjra1TEAAAAAuBlW5tzYypUr9eGHH8rf39/VUQAAAAC4\nGVbm3FiTJk20ePFiV8cAAAAA4IYoc24sKipKXl4sngIAAAC4EmUOAAAAAEyIMgcAAAAAJsQ5fJXE\nrQQAAAAAuBNW5txc48aNtWXLFlfHAAAAAOBmKHMAAAAAYEKUOQAAAAAwIT4zBwAAgBpt4MsbHT6H\nT+uvHDp+/9hsh44vSQ+vW+3wOVC9WJkDAAAAABOizAEAAACACXGaZSUNXT2mWsdb8/TCah0PAAAA\nwPWFMuemCgsLNWnSJJ07d04FBQUaPny4unXr5upYAAAAANwEZc5NffjhhwoODtb8+fOVmZmp3r17\nU0+MzMkAACAASURBVOYAAAAAWFHm3FSPHj0UFRUlSTIMQ56eni5OBAAAAMCdUObcVEBAgCQpNzdX\no0ePVkxMjIsTAQAAAHAnXM3SjSUnJ2vw4MHq1auXevbs6eo4AAAAANwIK3NuKjU1VcOGDdOUKVPU\noUMHV8cBAAAA4GYoc5Xk7FsJLF++XNnZ2Vq6dKmWLl0qSVq5cqX8/PycmgMAAACAe6LMuanJkydr\n8uTJro4BAAAAwE3xmTkAAAAAMCHKHAAAAACYEGUOAAAAAEyIMgcAAAAAJkSZAwAAAAAT4mqWlfTJ\n4KerdbyH162u1vEAAAAAXF8oc26suLhYkydP1k8//SSLxaJXX31VLVu2dHUsAAAAAG6A0yzdWGxs\nrCRp8+bNiomJ0YIFC1ycCAAAAIC7YGXOjXXv3l2dO3eWJCUlJalOnTquDQQAAADAbTi1zBUWFmrC\nhAk6d+6cPDw8NGPGDLVo0cKZEUzHy8tL48eP12effaZFixa5Og4AAAAAN+HU0yz37t2roqIibd68\nWSNGjNBbb73lzOlNa+7cudq5c6deeeUV5eXluToOAAAAADfg1JW5Zs2aqbi4WCUlJcrNzZWXl/3p\n4+PjnZDM+Srzfe3bt0/p6enq1auX8vLyVFhYqCNHjsjHx8cJCVEdqvP4feO949U2li0+rR0+hcPV\n1J8ZAAA4Gn+3ON+17nOnlrlatWrp3Llzeuihh5SRkaHly5fbfU9kZKQTklWCC24l0Lp1a02cOFFv\nvvmmioqKNG3aNHXo0MHpOcrlhH9Ba4JqPX7Z55XiNj8zAADug9+hlcLfLc5XmX1eUeFzaplbs2aN\n7rvvPo0dO1bJyckaMmSIPvroI/n6+jozhmnUqlVLCxcudHUMAAAAAG7IqWWuTp068vb2liQFBQWp\nqKhIxcXFzowAAAAAADWCU8vc0KFDNWnSJA0cOFCFhYV64YUXVKtWLWdGAAAAAIAawallLiAggNMG\nAQAAAKAaOPXWBAAAAACA6kGZAwAAAAAToswBAAAAgAlR5gAAAADAhChzAAAAAGBClDkAAAAAMCHK\nHAAAAACYEGUOAAAAAEyIMgcAAAAAJkSZAwAAAAAToswBAAAAgAlR5gAAAADAhChzAAAAAGBClDkA\nAAAAMCHKHAAAAACYEGUOAAAAAEyIMgcAAAAAJkSZAwAAAAAToswBAAAAgAlR5gAAAADAhChzAAAA\nAGBClDkAAAAAMCHKHAAAAACYEGUOAAAAAEyIMgcAAAAAJkSZAwAAAAAToswBAAAAgAlR5gAAAADA\nhChzAAAAAGBCXpV50YkTJ5SQkCAPDw81adJELVu2dHQuAAAAAEAFbJY5wzC0adMmrV27VgEBAWrY\nsKG8vLyUmJio3NxcDR48WAMGDJCHB4t7AAAAAOBsNsvc6NGj1bFjR23ZskVBQUFlnsvJydG2bds0\nYsQILVu2zOEhAQAAAABl2Sxzc+fOVa1atcp9rnbt2ho8eLAef/xxhwUDAAAAANhm8xzJ0iKXmZmp\n/fv3S5LefvttjR49WidPnizzGgAAAACAc9n9wNvYsWN1+vRp7d+/Xzt27FDXrl01depUZ2QDAAAA\nANhgt8xlZWVp0KBB2r17t/r06aPevXvr0qVLzsgGAAAAALDBbpkrKSnRd999p127dqlLly46duyY\niouLnZENAAAAAGCD3fvMjRs3TvPmzdPTTz+tiIgI9e/fXxMmTKjyhG+//bb27NmjwsJCPfnkk+rX\nr1+VxwIAAACA65XdMtehQwd16NDB+njLli1VniwuLk6HDh3Spk2bdOnSJa1atarKYwEAAADA9cxm\nmWvVqpUsFst/X+jlJQ8PDxUUFCgwMFAHDx686sn++c9/qmXLlhoxYoRyc3P18ssvVy01AAAAAFzn\nbJa548ePS5KmTp2qO+64Q48++qgsFot27typffv2VWmyjIwMJSUlafny5UpMTNTw4cO1Y8eOMqXx\n9+Lj46s0F+AOOH6dj30OAEDV8DvU+a51n9s9zfLo0aN69dVXrY+joqK0dOnSKk0WHBys5s2by8fH\nR82bN5evr6/S09MVFhZm8z2RkZFVmgsO9t5xVycwhWo9ftnnlcLPDADAFfgdWin83eJ8ldnnFRU+\nu1ez9Pf31wcffKC8vDzl5uZq48aNCg4OvrqU/xEZGal9+/bJMAxduHBBly5dqvJYAAAAAHA9s7sy\nN3/+fM2YMUMzZ86Uh4eHOnbsqHnz5lVpsi5duujgwYN6/PHHZRiGpkyZIk9PzyqNBQAAAADXM7tl\nbsuWLVq+fHm1TchFTwAAAADg2tk9zTI2NlaGYTgjCwAAAACgkuyuzAUHB6tHjx669dZb5evra90+\ne/ZshwYDAAAAANhmt8z16dPHGTkAAAAAAFehUmU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qJ598UnPmzHF1zBqhQYMGSkxMtD5e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bAAAG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\nAAAoIWUOAACghJQ5AACAEvoftIkx9sKxyy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aac9f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "fig, axs = plt.subplots(nrows=3,figsize=(15,15))\n",
    "\n",
    "iter1 = sns.barplot(ax=axs[0], x=\"trip_id\", order=[\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==1])\n",
    "iter2 = sns.barplot(ax=axs[1], x=\"trip_id\", order=[\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==2])\n",
    "iter3 = sns.barplot(ax=axs[2], x=\"trip_id\", order=[\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==3])\n",
    "\n",
    "axs[0].set_title('Iteration #1', fontsize=24,color=\"Gray\")\n",
    "axs[1].set_title('Iteration #2', fontsize=24,color=\"Gray\")\n",
    "axs[2].set_title('Iteration #3', fontsize=24,color=\"Gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "There are two types of iterations going on here: pathfinding and global.\n",
    "\n",
    "### Pathfinding Iterations\n",
    "Pathfinding iterations allow passengers who were denied boarding a chance to go back and see if any of the other paths in their choice-set have availability.  You can see that two passengers who probably wanted to initially board B2 decided to get on B3 and G4 after learning that B2 was full in the initial pathfinding iteration.\n",
    "\n",
    "### Global Iterations\n",
    "Global iterations go through a travel-time-updating step and then re-initiate pathfinding.  In this example, all the possible paths are found in the first iteration and there is not much additional wiggle room for a passenger to re-optimize.\n",
    "\n",
    "However, it looks like there is a tradeoff between G3 and R2 for the ten individuals who fit on the B2 bus.  They are  trading off in-vehicle and out of vehicle time.\n",
    "\n",
    "### Who missed the bus?\n",
    "\n",
    "It is evident that both B2 and B3 are at capacity, and so some of our friends didn't make it. Let's find out what that looks like in the path results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Person Level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pathset_paths_df = pd.read_csv(os.path.join(OUTPUT_DIR,OUTPUT_FOLDER,\"pathset_paths.csv\"), sep=\",\")\n",
    "pathset_paths_df = pathset_paths_df[pathset_paths_df[\"simulation_iteration\"]==1] #only want to look at the actual simulation iteration\n",
    "pd.set_option('display.max_colwidth',160) #widen so you can see whole description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>iteration</th>\n",
       "      <th>pathfinding_iteration</th>\n",
       "      <th>simulation_iteration</th>\n",
       "      <th>pathnum</th>\n",
       "      <th>probability</th>\n",
       "      <th>chosen</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter1.02 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter2.02 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter3.02 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>Mrs. Wuf</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unchosen</td>\n",
       "      <td>O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    person_id  iteration  pathfinding_iteration  simulation_iteration  pathnum  probability         chosen                                                                          description\n",
       "92   Mrs. Wuf          1                      1                     1        0          NaN       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "93   Mrs. Wuf          1                      1                     1        1          NaN       rejected    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D\n",
       "94   Mrs. Wuf          1                      1                     1        2          NaN       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "224  Mrs. Wuf          1                      2                     1        0          1.0  iter1.02 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "225  Mrs. Wuf          1                      2                     1        1          0.0       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "226  Mrs. Wuf          1                      2                     1        2          0.0       unchosen    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D\n",
       "340  Mrs. Wuf          2                      1                     1        0          NaN       rejected  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "341  Mrs. Wuf          2                      1                     1        1          NaN       unchosen    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D\n",
       "342  Mrs. Wuf          2                      1                     1        2          NaN       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "472  Mrs. Wuf          2                      2                     1        0          1.0  iter2.02 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "473  Mrs. Wuf          2                      2                     1        1          0.0       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "474  Mrs. Wuf          2                      2                     1        2          0.0       unchosen    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D\n",
       "588  Mrs. Wuf          3                      1                     1        0          NaN       rejected  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "589  Mrs. Wuf          3                      1                     1        1          NaN       unchosen    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D\n",
       "590  Mrs. Wuf          3                      1                     1        2          NaN       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "720  Mrs. Wuf          3                      2                     1        0          1.0  iter3.02 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "721  Mrs. Wuf          3                      2                     1        1          0.0       unchosen  O walk_access A local_bus Blue_B2 B transfer B2 local_bus Green_G3 C2 walk_egress D\n",
       "722  Mrs. Wuf          3                      2                     1        2          0.0       unchosen    O walk_access A local_bus Blue_B2 B transfer B1 local_bus Red_R2 C1 walk_egress D"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mswuf = pathset_paths_df[(pathset_paths_df['person_id']=='Mrs. Wuf')]\n",
    "mswuf[['person_id','iteration','pathfinding_iteration','simulation_iteration','pathnum','probability','chosen','description']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "As you can see, Mrs. Wuf originally tried to board the B2 but was rejected becuase it was full.  \n",
    "\n",
    "The next **pathfinding** iteration, she was able to board the B3 instead.\n",
    "\n",
    "However, in next large iteration, she re-tries the B2 [ why not? it is still a better path for her if she gets there before Shammond Williams] and is then redirected again to the B3.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_id</th>\n",
       "      <th>iteration</th>\n",
       "      <th>pathfinding_iteration</th>\n",
       "      <th>simulation_iteration</th>\n",
       "      <th>pathnum</th>\n",
       "      <th>probability</th>\n",
       "      <th>chosen</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter1.01 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter2.01 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>iter3.01 sim0</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rejected</td>\n",
       "      <td>O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         person_id  iteration  pathfinding_iteration  simulation_iteration  pathnum  probability         chosen                                                                          description\n",
       "121  Carlos Boozer          1                      1                     1        0          1.0  iter1.01 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "221  Carlos Boozer          1                      2                     1        0          NaN       rejected  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "369  Carlos Boozer          2                      1                     1        0          1.0  iter2.01 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "469  Carlos Boozer          2                      2                     1        0          NaN       rejected  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "617  Carlos Boozer          3                      1                     1        0          1.0  iter3.01 sim0  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D\n",
       "717  Carlos Boozer          3                      2                     1        0          NaN       rejected  O walk_access A local_bus Blue_B3 B transfer B2 local_bus Green_G4 C2 walk_egress D"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carlos = pathset_paths_df[(pathset_paths_df['person_id']=='Carlos Boozer')]\n",
    "carlos[['person_id','iteration','pathfinding_iteration','simulation_iteration','pathnum','probability','chosen','description']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carlos has a different issue: he only has one valid path: B3-->G4.\n",
    "When people like Mrs. Wuf get put on B3, they have priority (they got there first) over Carlos, so he gets booted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rejected_persons_df = pathset_paths_df[(pathset_paths_df['chosen']=='rejected')]\n",
    "rejected_persons_df = rejected_persons_df[['person_id']].drop_duplicates()\n",
    "\n",
    "rejected_persons_paths_df = pd.merge(pathset_paths_df, rejected_persons_df, on=['person_id'])\n",
    "rejected_persons_paths_df[['person_id','iteration','pathfinding_iteration','pathnum','probability','chosen','description']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QUESTION\n",
    "\n",
    "**Question 2-A** Who didn't make the trip by the end of iteration 3?\n",
    "\n",
    "**Question 2-B** What is updating for each pathfinding vs. global iteration?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a Route and Evaluate Cost of Crowding\n",
    "In this scenario, we will add a route that goes between the origin and destination without a transfer.  However, there is a longer wait up front and a longer walk for the egress.  The later departure time should work well for the Duke Blue Devils who don't leave the basketball court until a few minutes after the Tarheels and Wolfpack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-05-22 16:27:19 [INFO/MainProcess] Reading configuration file /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/input/demand-bigger/config_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Reading GTFS schedule\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       8           stops from                 stops.txt,              stops_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       4          routes from                routes.txt,             routes_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       1        agencies from                agency.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       0 fare attributes from       fare_attributes.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       0      fare rules from            fare_rules.txt,       fare_periods_ft.txt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dispersion': 0.5, 'overlap_variable': 'count', 'capacity': True, 'output_pathset_per_sim_iter': True}\n",
      "routes_ft.txt\n",
      "The file named routes_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "transfers_ft.txt\n",
      "The file named transfers_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "trips_ft.txt\n",
      "The file named trips_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "vehicles_ft.txt\n",
      "The file named vehicles_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n",
      "walk_access_ft.txt\n",
      "The file named walk_access_ft.txt was not expected.\n",
      "This may be a misspelled file name or the file may be included in a\n",
      "subdirectory. Please check spellings and make sure that there are no\n",
      "subdirectories within the feed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       2       transfers from             transfers.txt,          transfers_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       1        vehicles from           vehicles_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read      10           trips from                 trips.txt,              trips_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       1 service periods from              calendar.txt\n",
      "2017-05-22 16:27:19 [WARNING/MainProcess] Adding shape_dist_traveled in miles\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read      20      stop times from            stop_times.txt,         stop_times_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       5     walk access from        walk_access_ft.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       0            DAPs from drive_access_points_ft.txt\n",
      "2017-05-22 16:27:19 [WARNING/MainProcess] The following 3 stop ids have no walk access: \n",
      "  stop_id  taz\n",
      "1       B  NaN\n",
      "4      B1  NaN\n",
      "5      B2  NaN\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] -------- Reading demand --------\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Capacity constraint? 1\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read      26    person trips from             trip_list.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read      26         persons from                person.txt\n",
      "2017-05-22 16:27:19 [INFO/MainProcess] Read       3      households from             household.txt\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] Have 26 person trips\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] Wrote veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 1 **************************************\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:20 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:21 [INFO/MainProcess] Wrote pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:21 [INFO/MainProcess] Wrote pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:21 [INFO/MainProcess] Wrote performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:21 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:21 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:21 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]           Need to bump 16 passengers from 1 trip-stops\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 16 chosen paths\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:22 [INFO/MainProcess] Wrote pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:22 [INFO/MainProcess] Wrote pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:22 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:22 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:22 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:23 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:23 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:23 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:23 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]           Rejecting 16 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,     16 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Wrote chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Wrote chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] \n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   ARRIVED PASSENGERS:                10\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   MISSED PASSENGERS:                 16\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]   CAPACITY GAP:                61.53846\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 2 **************************************\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Finding pathsets for 16 trips\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       1 paths sought,      1 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       2 paths sought,      2 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       3 paths sought,      3 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       4 paths sought,      4 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       5 paths sought,      5 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       6 paths sought,      6 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       7 paths sought,      7 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       8 paths sought,      8 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]       9 paths sought,      9 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      10 paths sought,     10 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      11 paths sought,     11 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      12 paths sought,     12 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      13 paths sought,     13 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      14 paths sought,     14 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      15 paths sought,     15 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess]      16 paths sought,     16 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:24 [INFO/MainProcess] Finished finding     16 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:25 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:25 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:25 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:25 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:25 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:25 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:25 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:25 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:25 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:25 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,      0 fully rejected and     16 needing a choice\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           Chose 16 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:26 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:26 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:26 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:26 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:26 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]           Have     26 total passenger-trips, with     26 chosen paths,      0 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:27 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] \n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] ***************************** ITERATION 1 PATHFINDING ITERATION 3 **************************************\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Finding pathsets for 0 trips\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] No rows of performance_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] \n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:28 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 1 **************************************\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:28 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:29 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:29 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]           Need to bump 16 passengers from 1 trip-stops\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 16 chosen paths\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:30 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:31 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:31 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:31 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:31 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:31 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]           Rejecting 16 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,     16 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] \n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   ARRIVED PASSENGERS:                10\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   MISSED PASSENGERS:                 16\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]   CAPACITY GAP:                61.53846\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 2 **************************************\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:32 [INFO/MainProcess] Finding pathsets for 16 trips\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]       1 paths sought,      1 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:32 [INFO/MainProcess]       2 paths sought,      2 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       3 paths sought,      3 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       4 paths sought,      4 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       5 paths sought,      5 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       6 paths sought,      6 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       7 paths sought,      7 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       8 paths sought,      8 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]       9 paths sought,      9 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      10 paths sought,     10 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      11 paths sought,     11 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      12 paths sought,     12 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      13 paths sought,     13 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      14 paths sought,     14 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      15 paths sought,     15 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]      16 paths sought,     16 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] Finished finding     16 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:33 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:33 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,      0 fully rejected and     16 needing a choice\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           Chose 16 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:34 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:35 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:35 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:35 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:35 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:35 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]           Have     26 total passenger-trips, with     26 chosen paths,      0 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] \n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:36 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] ***************************** ITERATION 2 PATHFINDING ITERATION 3 **************************************\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Finding pathsets for 0 trips\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] No rows of performance_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:36 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] \n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 1 **************************************\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Finding pathsets for 26 trips\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       1 paths sought,      1 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       2 paths sought,      2 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       3 paths sought,      3 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       4 paths sought,      4 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       5 paths sought,      5 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       6 paths sought,      6 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       7 paths sought,      7 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       8 paths sought,      8 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]       9 paths sought,      9 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      10 paths sought,     10 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      11 paths sought,     11 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      12 paths sought,     12 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      13 paths sought,     13 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      14 paths sought,     14 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      15 paths sought,     15 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      16 paths sought,     16 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      17 paths sought,     17 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      18 paths sought,     18 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      19 paths sought,     19 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      20 paths sought,     20 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      21 paths sought,     21 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      22 paths sought,     22 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      23 paths sought,     23 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      24 paths sought,     24 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      25 paths sought,     25 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]      26 paths sought,     26 paths found of 26 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Finished finding     26 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 1 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:37 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:37 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]           Have     26 total passenger-trips, with      0 chosen paths,      0 fully rejected and     26 needing a choice\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]           Chose 26 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]           Need to bump 16 passengers from 1 trip-stops\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]         -> completed loop bump_iter 0 and bumped 16 chosen paths\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:38 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:39 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:39 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:39 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:39 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:39 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len     78);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]           Rejecting 16 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,     16 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] \n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   ARRIVED PASSENGERS:                10\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   MISSED PASSENGERS:                 16\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]   CAPACITY GAP:                61.53846\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 2 **************************************\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:40 [INFO/MainProcess] Finding pathsets for 16 trips\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]       1 paths sought,      1 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]       2 paths sought,      2 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]       3 paths sought,      3 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:40 [INFO/MainProcess]       4 paths sought,      4 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]       5 paths sought,      5 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]       6 paths sought,      6 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]       7 paths sought,      7 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]       8 paths sought,      8 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]       9 paths sought,      9 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      10 paths sought,     10 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      11 paths sought,     11 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      12 paths sought,     12 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      13 paths sought,     13 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      14 paths sought,     14 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      15 paths sought,     15 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]      16 paths sought,     16 paths found of 16 paths total.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] Finished finding     16 passenger paths.  Time elapsed:  0h: 0m: 0s\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] Appended performance_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 2 *** SIMULATING ***********************\n",
      "2017-05-22 16:27:41 [INFO/MainProcess] Simulation Iteration 0\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:41 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           Have     26 total passenger-trips, with     10 chosen paths,      0 fully rejected and     16 needing a choice\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           Chose 16 out of 26 paths from the pathsets => total chosen 26\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:42 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:42 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:43 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:43 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:43 [INFO/MainProcess] Simulation Iteration 1\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]   Step 1. Find out board/alight times for all pathset links from vehicle times\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]   Step 2. Flag missed transfer links and paths in the pathsets\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]           flag_missed_transfers found 0 missed transfer trip legs for 0 paths\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]   Step 3. Calculate costs and probabilities for all pathset paths\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]           Adding fares to pathset\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]           Calculating overlap for chunk    1/   1 (len    190);  mem_use=Uknown; please install python package psutil\n",
      "2017-05-22 16:27:43 [INFO/MainProcess]   Step 4. Choose a path for each passenger from their pathset\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]           Rejecting 0 previously chosen paths for huge costs\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]           Have     26 total passenger-trips, with     26 chosen paths,      0 fully rejected and      0 needing a choice\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Step 5. Put passenger paths on transit vehicles to get vehicle boards/alights/load and assess capacity constraints\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Step 5.1 Put passengers on transit vehicles.\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Step 5.2 Capacity constraints on transit vehicles.\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]           Bumping one at a time? false\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]           No over-capacity vehicles\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Step 6. Update dwell and travel times for transit vehicles\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Step 7. Write pathsets (paths and links)\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended pathset_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_paths.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended pathset_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathset_links.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   No more path choices to make => Ending simulation loop\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended chosen_links_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_links.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended chosen_paths_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/chosenpaths_paths.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] \n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] ***************************** ITERATION 3 PATHFINDING ITERATION 3 **************************************\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] **************************** GENERATING PATHS **********************************************************\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Finding paths for trips for those that haven't arrived yet\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Finding pathsets for 0 trips\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] No rows of pathset_paths_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_paths.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] No rows of pathset_links_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/pathsfound_links.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] No rows of performance_df dataframe to write to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/ft_output_performance.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Appended veh_trips_df dataframe to /Users/elizabeth/Documents/urbanlabs/MTC/SHRP2/tutorial/tta/output/capacity-demand-wNewRoute/veh_trips.csv\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] \n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Length of trip list:               26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   Number of pathsets found:          26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   ARRIVED PASSENGERS:                26\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   MISSED PASSENGERS:                  0\n",
      "2017-05-22 16:27:44 [INFO/MainProcess]   CAPACITY GAP:                 0.00000\n",
      "2017-05-22 16:27:44 [INFO/MainProcess] Successfully completed!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'capacity_gap': 0.0,\n",
       " 'passengers_arrived': 26,\n",
       " 'passengers_demand': 26,\n",
       " 'passengers_missed': 0,\n",
       " 'paths_found': 26}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INPUT_NETWORKS_NEW_ROUTE   = os.path.join(BASE_DIR,\"tta\",\"input\",\"network-added-route\")\n",
    "OUTPUT_DIR   = os.path.join(BASE_DIR,\"tta\",\"output\")\n",
    "OUTPUT_FOLDER_NEW_ROUTE    = r\"capacity-demand-wNewRoute\"\n",
    "\n",
    "Run.run_fasttrips(input_network_dir= INPUT_NETWORKS_NEW_ROUTE,\n",
    "                  input_demand_dir = INPUT_DEMAND,\n",
    "                  run_config       = RUN_CONFIG,\n",
    "                  input_weights    = INPUT_WEIGHTS,\n",
    "                  output_dir       = OUTPUT_DIR,\n",
    "                  output_folder    = OUTPUT_FOLDER_NEW_ROUTE,\n",
    "                  iters            = 3,\n",
    "                  overlap_variable = \"count\",\n",
    "                  dispersion       = 0.5,\n",
    "                  capacity         = True,\n",
    "                  output_pathset_per_sim_iter = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew, it looks like everybody made it this time!  Let's take a look at how the ridership turned out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11e3ea9d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rm2++qd9+++2qa/zHP/4hSYqLi1NiYuIVt3P8+HF98803xdoEAMAewhwAoFJ7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\nBwAAAAAWRJkDAAAAAAuizAEAAACABf0HYVmw94dY9vIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e032e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth',160) \n",
    "full_output_directory=os.path.join(OUTPUT_DIR,OUTPUT_FOLDER_NEW_ROUTE)\n",
    "vehicles_df = pd.read_csv(os.path.join(full_output_directory,\"veh_trips.csv\"), \n",
    "                                       sep=\",\", \n",
    "                                       parse_dates=['arrival_time', 'departure_time'],\n",
    "                                       date_parser=lambda x: datetime.datetime.strptime(x, '%H:%M:%S') )\n",
    "\n",
    "\n",
    "vehicles_df[['iteration','pathfinding_iteration','arrival_time','departure_time','route_id','trip_id','stop_id','boards','onboard','standees']]\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "fig, axs = plt.subplots(nrows=3,figsize=(15,15))\n",
    "\n",
    "iter1 = sns.barplot(ax=axs[0], x=\"trip_id\", order=[\"O1\",\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==1])\n",
    "iter2 = sns.barplot(ax=axs[1], x=\"trip_id\", order=[\"O1\",\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==2])\n",
    "iter3 = sns.barplot(ax=axs[2], x=\"trip_id\", order=[\"O1\",\"B1\",\"B2\",\"B3\",\"G1\",\"R1\",\"G2\",\"R2\",\"G3\",\"G4\"],y=\"boards\", hue=\"pathfinding_iteration\",  estimator=sum, ci=None, data=vehicles_df[vehicles_df[\"iteration\"]==3])\n",
    "\n",
    "axs[0].set_title('Iteration #1', fontsize=24,color=\"Gray\")\n",
    "axs[1].set_title('Iteration #2', fontsize=24,color=\"Gray\")\n",
    "axs[2].set_title('Iteration #3', fontsize=24,color=\"Gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well it looks as though the new \"Orange Express\" is a winner!  It is the clear favorite in each of the first path-finding iterations and then ridership spreads out to accommodate second and third priorities.\n",
    "\n",
    "One of the interesting analyses that you can do in this regard is to evaluate the effect of the disutility of having to take a \"next-best\" path rather than your optimal path due to crowding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pathset_paths_df = pd.read_csv(os.path.join(OUTPUT_DIR,OUTPUT_FOLDER,\"pathset_paths.csv\"), sep=\",\")\n",
    "pathset_paths_df = pathset_paths_df[pathset_paths_df[\"simulation_iteration\"]==1] #only want to look at the actual simulation iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import graphs_ft\n",
    "graphs_ft.choice_set_facet_plot_single_pax(pathset_paths_df,pax=\"Mrs. Wuf\",value=\"sim_cost\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QUESTIONS\n",
    "\n",
    "**Question 2-C** What is the new preferred route by most of the travellers?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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