Calculate the number of departures and mean headways for all stops within a given timespan and for given service_ids.
get_stop_frequency(
gtfs_obj,
start_time = "06:00:00",
end_time = "22:00:00",
service_ids = NULL,
by_route = TRUE
)gtfs feed (tidygtfs object)
analysis start time, can be given as "HH:MM:SS", hms object or numeric value in seconds.
analysis period end time, can be given as "HH:MM:SS", hms object or numeric value in seconds.
A set of service_ids from the calendar dataframe identifying a particular service id. If not provided, the service_id with the most departures is used.
Default TRUE, if FALSE then calculate headway for any line coming through the stop in the same direction on the same schedule.
dataframe of stops with the number of departures and the headway (departures divided by timespan) in seconds as columns
Some GTFS feeds contain a frequency data frame already. Consider using this instead, as it will be more accurate than what tidytransit calculates.
data(gtfs_duke)
stop_frequency <- get_stop_frequency(gtfs_duke)
x <- order(stop_frequency$mean_headway)
head(stop_frequency[x,])
#> # A tibble: 6 × 6
#> stop_id route_id direction_id service_id n_departures mean_headway
#> <chr> <chr> <int> <chr> <int> <dbl>
#> 1 778127 1683 0 c_876_b_21969_d_31 143 403
#> 2 778084 1683 0 c_876_b_21969_d_31 128 450
#> 3 778095 1690 0 c_876_b_21969_d_31 78 738
#> 4 778134 1679 0 c_876_b_21969_d_31 76 758
#> 5 778058 1690 0 c_876_b_21969_d_31 75 768
#> 6 778102 1690 0 c_876_b_21969_d_31 75 768