Metroscore is a Python package for analyzing transit quality in a region. It compares the accessibility of driving to the accessibility of public transit options (walking, biking, & public transit) for any given time, place, and trip length. The result is a collection of analyses for a region that can be analyzed to understand how spatial and temporal constraints affect transit performance.
Metroscore is available on the Python Package Index (PyPi). To install, run:
pip install metroscoregit clone https://www.github.com/agupta01/metroscore
cd metroscore
pip install -r requirements.txt
pip install -e .The following describes the most basic usage of Metroscore. For advanced usage, including configuration options, please see the docs.
- GTFS: public transit agencies frequently publish their transit schedules in the General Transit Feed Specification (GTFS) format. This is a standard format for describing transit schedules and routes.
metroscoreuses the GTFS format to generate transit service areas.
The first step of running any Metroscore analysis is to build the transit and drive datasets. To do so:
from metroscore.metroscore import Metroscore
m = Metroscore(name="Brooklyn, NY")
m.build_drive()
m.build_transit(metro="./data/mta_metro_gtfs", bus="./data/mta_bus_gtfs")With a built object, you can now pass in points, times of day, and trip durations to run an analysis:
from metroscore.utils import start_time_to_seconds
start_times = list(map(start_time_to_seconds, ["7AM", "12PM", "4PM", "9PM"]))
results = m.compute(
points=[(<lat>, <lon>), (<lat>, <lon>), ...],
time_of_days=start_times,
cutoffs=[600, 1200, 1800, 2400, 3000] # 10, 20, 30, 40, 50 minutes
)Results may either be read directly as the return value of compute(), or by getting the _results object from the Metroscore object.
m.get_score(location=(<lat>, <lon>), time_of_day=start_time_to_seconds("7AM"), cutoff=600)