Usage: 3.3. Writing Data: GTFS

Writing GTFS data

Available as a jupyter notebook or wiki page.

GeNet can generate CSV or text files in GTFS-like format based on a Schedule object. It will return the following file

• calendar.txt
• stop_times.txt
• stops.txt
• trips.txt
• routes.txt (or .csv depending on the method used)

When reading a GTFS feed, GeNet expects a date in YYYYMMDD format. It does not retain this information. You are required to pass this GTFS day when exporting to GTFS. Schedule object does not contain a lot of the optional GTFS data point (a lot more is retained if the Schedule was created through GeNet). In particular, if using MATSim files as input, your Schedule will mostly contain only the required fields. Other important remarks:

• list of network link IDs associated with the transit routes as the path and the relation of stops to network link IDs is not exported.
• agency.txt is not generated
• service_id is generated from Service objects. If the Schedule object is generated from GTFS through GeNet, those service IDs are based off of the route_id field in GTFS. This means the two fields in output GTFS are the same. The transit route split of services (based on ordered chain of stops) is lost when exporting to GTFS.
• you can pass you own mode_to_route_type dictionary that will map modes in the Schedule to the route_type codes you want. Otherwise it will default to

{
    "tram": 0, "subway": 1, "rail": 2, "bus": 3, "ferry": 4, "cablecar": 5, "gondola": 6, "funicular": 7
}

based on this. Caveat - if you have read Schedule from GTFS, the route_type codes are retained and will not be changed.

Here is the reference page for the schema of GTFS data.

from genet import read_gtfs
s = read_gtfs('../example_data/example_gtfs', '20190603')
s

2022-07-14 15:32:40,982 - Reading GTFS from ../example_data/example_gtfs
2022-07-14 15:32:40,983 - Reading the calendar for GTFS
2022-07-14 15:32:40,988 - Reading GTFS data into usable format
2022-07-14 15:32:40,989 - Reading stop times
2022-07-14 15:32:40,997 - Reading trips
2022-07-14 15:32:41,003 - Reading stops
2022-07-14 15:32:41,007 - Reading routes





<Schedule instance at 140268498459024: with 2 services>

You can export Schedule straight to files:

s.write_to_csv('../example_data/output_gtfs', '20190603')

2022-07-14 15:32:41,111 - Saving Schedule to GTFS csv in ../example_data/output_gtfs
2022-07-14 15:32:41,200 - Saving ../example_data/output_gtfs/stops.csv
2022-07-14 15:32:41,212 - Saving ../example_data/output_gtfs/routes.csv
2022-07-14 15:32:41,218 - Saving ../example_data/output_gtfs/trips.csv
2022-07-14 15:32:41,221 - Saving ../example_data/output_gtfs/stop_times.csv
2022-07-14 15:32:41,227 - Saving ../example_data/output_gtfs/calendar.csv

s.write_to_gtfs('../example_data/output_gtfs', '20190603')

2022-07-14 15:32:41,242 - Saving Schedule to GTFS txt in ../example_data/output_gtfs
2022-07-14 15:32:41,303 - Saving ../example_data/output_gtfs/stops.txt
2022-07-14 15:32:41,305 - Saving ../example_data/output_gtfs/routes.txt
2022-07-14 15:32:41,308 - Saving ../example_data/output_gtfs/trips.txt
2022-07-14 15:32:41,311 - Saving ../example_data/output_gtfs/stop_times.txt
2022-07-14 15:32:41,313 - Saving ../example_data/output_gtfs/calendar.txt

Or generate pandas.DataFrame GTFS tables:

gtfs = s.to_gtfs('20190603')

gtfs.keys()

dict_keys(['stops', 'routes', 'trips', 'stop_times', 'calendar'])

gtfs['stops'].head()

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	stop_id	stop_name	stop_lat	stop_lon	stop_code	stop_desc	zone_id	stop_url	location_type	parent_station	stop_timezone	wheelchair_boarding	level_id	platform_code
BSE	BSE	Bus Stop snap to edge	51.522686	-0.141362	NaN	NaN	NaN	NaN	0.0	210G433	NaN	NaN	NaN	NaN
RSE	RSE	Rail Stop snap to edge	51.519261	-0.142159	NaN	NaN	NaN	NaN	0.0	210G431	NaN	NaN	NaN	NaN
RSN	RSN	Rail Stop snap to node	51.523134	-0.141095	NaN	NaN	NaN	NaN	0.0	210G430	NaN	NaN	NaN	NaN
BSN	BSN	Bus Stop snap to node	51.521620	-0.140053	NaN	NaN	NaN	NaN	0.0	210G432	NaN	NaN	NaN	NaN