Realtime csp input adapter for the MTA's GTFS data feed. Feeds covered are all lines of the New York City subway, LIRR and Metro North railroads
Setup:
pip install csp requests pandas protobuf
The example script e_01_nyct_subway.py prints a realtime departure board for a set of platforms. The 'platforms' are specified as a pair of stop_id:line combinations. The stop_id corresponding to a specific station/service can be found in file stops.txt.
For example, at 14 St - Union Square the stop_id for the Lexington Ave (4/5/6) line is 635 (35th stop on the 6 line), the stop_id for the Broadway line (N/Q/R/W) trains is R20 (20th stop on the R line), and the stop_id for the BMT Canarsie line (L) is L03. Thus, to see the next 5 trains for all platforms at Union Square, you could run:
>> python e_01_nyct_subway.py 635:456 L03:L R20:NQRW --num_trains 5
2024-04-20 00:27:24.166629 Departure Board:
At station 14 St-Union Sq
Uptown L train to 8 Av in 1 minutes
Downtown L train to Canarsie-Rockaway Pkwy in 1 minutes
Uptown L train to 8 Av in 2 minutes
Uptown L train to 8 Av in 5 minutes
Downtown L train to Canarsie-Rockaway Pkwy in 5 minutes
2024-04-20 00:27:24.187608 Departure Board:
At station 14 St-Union Sq
Downtown Q train to Coney Island-Stillwell Av in 1 minutes
Uptown W train to Astoria-Ditmars Blvd in 2 minutes
Downtown W train to Whitehall St-South Ferry in 3 minutes
Downtown N train to Coney Island-Stillwell Av in 5 minutes
Uptown R train to Forest Hills-71 Av in 6 minutes
2024-04-20 00:27:24.224544 Departure Board:
At station 14 St-Union Sq
Uptown 5 train to Eastchester-Dyre Av in 1 minutes
Downtown 6 train to Brooklyn Bridge-City Hall in 2 minutes
Uptown 6 train to Pelham Bay Park in 4 minutes
Uptown 4 train to Woodlawn in 6 minutes
Downtown 6 train to Brooklyn Bridge-City Hall in 8 minutes
The MTA also exposes realtime accessibility information about elevator/escalator outages at their stations. In e_02_realtime_accessibility.py we access this data through a JSON adapter and compute some basic stats on the current state of subway accessibility.
>> python e_02_realtime_accessibility.py
2024-04-20 14:32:22.075735 Current Accessibility Status:
Total elevator outages: 26
ADA critical elevator outages: 3
Realtime Accessible Stations: 110 of 472
Average Time per Outage: 139 days
Alerts for all MTA services are published as both GTFS and JSON feeds. In e_03_bus_alerts.py we leverage the JSON feed to print all current bus alerts.
>> python e_03_bus_alerts.py
2024-04-20 16:04:17.556450 Realtime Alerts:
-- B103: B25, B26, B38, B41 B52, and B103 buses may experience delays in the Downton Brooklyn area and will wait out any temporary closures
-- B103: B25, B26, B38, B41, B52 and B103 stops on Joralemon St at Court St and Joralemon St at Adams St are closed
-- B15: Eastbound B15 buses may experience delays near JFK AirTrain and will wait out any temporary closures
-- B16: B16 stops on Ocean Ave between Parkside Ave and Empire Blvd, on Lincoln Rd, and Flatbush Ave at Ocean Ave will be closed
...
Since the MTA does not directly provide historical data, we include a script record_data.py which we used to record incoming GTFS/JSON messages and write them to a Parquet file. Then, we read back in the Parquet file to expose historical feed messages to the graph. We can use this historical data to gather insights and test realtime apps. A basic example of this is e_04_average_wait_time.py, which computes the hourly average wait time for 12 hours of recorded data on the night of April 21st, 2024.
We also leverage csp.stats in this example to compute the hourly mean wait times and standard deviation. csp.stats is a useful module for rolling time-series computations and contains almost all statistics functions. Lastly, we display the data using matplotlib.
>> python e_04_average_wait_time.py --filename recorded_data/2024-04-21-18\:54_to_2024-04-22-06\:54/1234567S_20240421_1854.parquet --stop_id 230
