The feed Module

Some tools for computing stats from a GTFS feed, assuming the feed is valid.

All time estimates below were produced on a 2013 MacBook Pro with a 2.8 GHz Intel Core i7 processor and 16GB of RAM running OS 10.9.2.

class gtfs_toolkit.feed.Feed(path)

Bases: builtins.object

A class to gather all the GTFS files for a feed and store them in memory as Pandas data frames. Make sure you have enough memory! The stop times object can be big.

dump_all_stats(directory, dates=None, freq='1H')

Into the given directory, dump to separate CSV files the outputs of

• self.get_stops_stats(dates)
• self.get_stops_time_series(dates)
• trips_stats = self.get_trips_stats()
• self.get_routes_stats(trips_stats, dates)
• self.get_routes_time_series(dates)

where each time series is resampled to the given frequency. Also include a README.txt file that contains a few notes on units and include some useful charts.

If no dates are given, then use self.get_first_week()[:5].

get_dates()

Return a chronologically ordered list of dates (datetime.date objects) for which this feed is valid.

get_first_week()

Return a list of dates (datetime.date objects) of the first Monday–Sunday week for which this feed is valid. In the unlikely event that this feed does not cover a full Monday–Sunday week, then return whatever initial segment of the week it does cover.

get_linestring_by_shape()

Return a dictionary with structure shape_id -> Shapely linestring of shape in UTM coordinates. If self.shapes is None, then return None.

get_routes_stats(trips_stats, dates, split_directions=True)

Take trips_stats, which is the output of self.get_trips_stats(), and use it to calculate stats for all the routes active on at least one day of the given dates (list of datetime.date objects).

Return a Pandas data frame with the following columns

• route_id
• direction_id
• mean_daily_num_trips
• min_start_time: start time of the earliest active trip on the route
• max_end_time: end time of latest active trip on the route
• max_headway: maximum of the durations (in seconds) between trip starts on the route between 07:00 and 19:00 on the given dates
• mean_headway: mean of the durations (in seconds) between trip starts on the route between 07:00 and 19:00 on the given dates
• mean_daily_duration: in hours
• mean_daily_distance: in kilometers; contains all np.nan entries if self.shapes is None
• mean_daily_speed: in kilometers per hour

If split_directions == False, then remove the direction_id column and compute each route’s stats based using its trips running in both directions. Note that this will give bidirectional headway stats, which most folks don’t find useful.

NOTES:

Takes about 0.2 minutes on the Portland feed given the first five weekdays of the feed.

get_routes_time_series(trips_stats, dates, split_directions=True, freq='5Min')

Given trips_stats, which is the output of self.get_trips_stats(), return a time series version of the following route stats for the given dates:

• mean daily number of vehicles in service by route ID
• mean daily number of trip starts by route ID
• mean daily service duration in hours by route ID
• mean daily service distance in kilometers by route ID
• mean daily speed in kilometers per hour

The time series is a Pandas data frame with a period index for a 24-hour period sampled at the given frequency. The maximum allowable frequency is 1 minute. If multiples dates are given, a generic placeholder date of 2001-01-01 is used as the date for the period index. Otherwise, the given date is used.

The columns of the data frame are hierarchical (multi-index) with

• top level: name = ‘statistic’, values = [‘mean_daily_distance’, ‘mean_daily_duration’, ‘mean_daily_num_trip_starts’, ‘mean_daily_num_vehicles’, ‘mean_daily_speed’]
• middle level: name = ‘stop_id’, values = the active stop IDs
• bottom level: name = ‘direction_id’, values = 0s and 1s

If split_directions == False, then don’t include the bottom level.

NOTES:

• To resample the resulting time series use the following methods:

  • for ‘mean_daily_num_vehicles’ series, use how=np.mean
  • for the other series, use how=np.sum
  • ‘mean_daily_speed’ can’t be resampled and must be recalculated from ‘mean_daily_distance’ and ‘mean_daily_duration’

• To remove the placeholder date (2001-1-1) and seconds from the time series f, do f.index = [t.time().strftime('%H:%M') for t in f.index.to_datetime()]

• Takes about 0.6 minutes on the Portland feed given the first five weekdays of the feed.

get_stations_stats(dates, split_directions=True)

If this feed has station data, that is, ‘location_type’ and ‘parent_station’ columns in self.stops, then compute the same stats that self.get_stops_stats() does, but for stations. Otherwise, return None.

NOTES:

Takes about 0.2 minutes on the Portland feed given the first five weekdays of the feed.

get_stops_activity(dates)

Return a Pandas data frame with the columns

• stop_id
• dates[0]: a series of ones and zeros indicating if a

stop has stop times on this date (1) or not (0) ... - dates[-1]: ditto

If dates is None, then return None.

get_stops_in_stations()

If this feed has station data, that is, ‘location_type’ and ‘parent_station’ columns in self.stops, then return a Pandas data frame that has the same columns as self.stops but only includes stops with parent stations, that is, stops with location type 0 or blank and nonblank parent station. Otherwise, return None.

get_stops_stats(dates, split_directions=True)

Return a Pandas data frame with the following columns:

• stop_id
• direction_id
• mean_daily_num_vehicles: mean daily number of vehicles visiting stop
• max_headway: maximum of the durations (in seconds) between vehicle departures at the stop between 07:00 and 19:00 on the given dates
• mean_headway: mean of the durations (in seconds) between vehicle departures at the stop between 07:00 and 19:00 on the given dates
• min_start_time: earliest departure time of a vehicle from this stop over the given date range
• max_end_time: latest departure time of a vehicle from this stop over the given date range

If split_directions == False, then compute each stop’s stats using vehicles visiting it from both directions.

NOTES:

Takes about 0.73 minutes on the Portland feed given the first five weekdays of the feed.

get_stops_time_series(dates, split_directions=True, freq='5Min')

Return a time series version of the following stops stats for the given dates:

• mean daily number of vehicles by stop ID

The time series is a Pandas data frame with a period index for a 24-hour period sampled at the given frequency. The maximum allowable frequency is 1 minute. If multiples dates are given, a generic placeholder date of 2001-01-01 is used as the date for the period index. Otherwise, the given date is used.

The columns of the data frame are hierarchical (multi-index) with

• top level: name = ‘statistic’, values = [‘mean_daily_num_vehicles’]
• middle level: name = ‘stop_id’, values = the active stop IDs
• bottom level: name = ‘direction_id’, values = 0s and 1s

If split_directions == False, then don’t include the bottom level.

NOTES:

• ‘mean_daily_num_vehicles’ should be resampled with how=np.sum
• To remove the placeholder date (2001-1-1) and seconds from the time series f, do f.index = [t.time().strftime('%H:%M') for t in f.index.to_datetime()]
• Takes about 6.15 minutes on the Portland feed given the first five weekdays of the feed.

get_trips_activity(dates)

Return a Pandas data frame with the columns

• trip_id
• route_id
• direction_id
• dates[0]: a series of ones and zeros indicating if a

trip is active (1) on the given date or inactive (0) ... - dates[-1]: ditto

If dates is None, then return None.

get_trips_stats()

Return a Pandas data frame with the following columns:

• trip_id
• direction_id
• route_id
• start_time: first departure time of the trip
• end_time: last departure time of the trip
• start_stop_id: stop ID of the first stop of the trip
• end_stop_id: stop ID of the last stop of the trip
• duration: duration of the trip in hours
• distance: distance of the trip in kilometers; contains all np.nan entries if self.shapes is None

NOTES:

Takes about 1 minute on the Portland feed.

get_xy_by_stop()

Return a dictionary with structure stop_id -> stop location as a UTM coordinate pair

is_active_trip(trip, date)

If the given trip (trip ID) is active on the given date (date object), then return True. Otherwise, return False. To avoid error checking in the interest of speed, assume trip is a valid trip ID in the feed and date is a valid date object.