Shared Parking Analysis Tool

Quick start

1. Install conda via Anaconda or Miniconda
2. Clone this repository and navigate to the project root directory
3. Run conda env create -f environment.yml && conda activate shared_parking
4. Run python run_model.py -c tests/winooski_example/configuration.yaml to use the example configuration

Customization

1. Copy tests/winooski_example to a new directory and edit configuration.yaml to customize inputs and other parameters
2. Add an -s flag to the run command to specify individual steps, ex: python run_model.py -c <your directory>/configuration.yaml -s factors preference
3. Run python run_model.py -h for the full helptext

Steps

Tool contains three model steps, run sequentially. Inputs and outputs for the Winooski scenario can be found in the tests directory. Timestamped logfiles are written to the logs/ output subdirectory.

Generate Parking Factors

Creates factors.csv, a csv of the factors dataframe. The factors dataframe contains every combination of factors related to shared parking.

Inputs

• Factors XLSX: Parking Demand and Adjustments.xlsx. Contains three sheets for Land Use, Monthly Usage, and TOD (time of day) factors

Outputs

• factors.csv a CSV of combined monthly, daily, and hourly demand factors

Generate Parking Preference

This script takes a parking demand generator shapefile and a parking supply shapefile as input. The parking generators (demand) are joined to the nearest lots (supply) within a certain buffer distance. The joined file is saved to the outputs directory.

Inputs

• Demand Shapefile: Winooski_Demand_Generators.shp.
• Supply Shapefile: Winooski_Parking_Supply.shp.

Outputs

• Parking Preference CSV: parking_preference.csv.

Generate Parking Demand

This script assigns demand from the generators, using the land use/time of day factors and parking preference lists to distribute parking among all of the lots for each time-slot defined in the factors file.

Inputs

• Factors DataFrame from Generate Parking Factors
• Parking Preference DataFrame from Generate Parking Preference

Outputs

• Parking Distribution: timeseries.csv.