Trips per day

Feeds can be given as paths to their .zip files. In this case, the large shapes and stop_times tables are not read. By default, the function returns one row per feed and service day:

spo_path <- system.file("extdata/spo_gtfs.zip", package = "gtfstools")
poa_path <- system.file("extdata/poa_gtfs.zip", package = "gtfstools")

daily_trips <- get_calendar_overlap(c(spo_path, poa_path))

daily_trips |> 
  dplyr::filter( overlap == TRUE) |> 
  head()
#>        feed       date n_trips overlap
#>      <char>     <Date>   <int>  <lgcl>
#> 1: spo_gtfs 2019-01-18    7948    TRUE
#> 2: spo_gtfs 2019-01-19    7945    TRUE
#> 3: spo_gtfs 2019-01-20    7945    TRUE
#> 4: spo_gtfs 2019-01-21    7948    TRUE
#> 5: spo_gtfs 2019-01-22    7948    TRUE
#> 6: spo_gtfs 2019-01-23    7948    TRUE

n_trips is the number of trips that run on the day, and overlap tells whether all feeds have service on it. Trips listed in frequencies count once per departure, so feeds that describe their service with frequencies are comparable with feeds that list every trip.

A feed has service on a day when at least one of its services runs on it, according to the weekdays and dates in calendar and the exceptions in calendar_dates. Services not used by any trip are ignored. Feeds already read with read_gtfs() (and possibly edited afterwards) can also be given as a list, whose names are used as feed names, e.g. get_calendar_overlap(list(spo = spo_gtfs, poa = poa_gtfs)).

With plot = TRUE, the density of each feed’s trips over time is plotted in a panel of its own, weighting each day by its number of trips. The days on which all feeds have service are shaded:

get_calendar_overlap(c(spo_path, poa_path), plot = TRUE) +
  coord_cartesian(xlim = as.Date(c("2019-01-01", "2019-05-01")))

The result is a regular ggplot object, so it can be customised with {ggplot2} functions, e.g. to zoom in on a period, as above.

The daily trips help pick a departure date on which every feed runs its usual level of service, such as a regular weekday away from holidays. For example, the trips each feed runs on the first Wednesday on which both have service (format(x, "%u") numbers weekdays from 1, Monday, regardless of the locale):

overlap_days <- unique(daily_trips$date[daily_trips$overlap])
departure_date <- overlap_days[format(overlap_days, "%u") == "3"][1]

daily_trips |>
  filter(date == departure_date)
#>        feed       date n_trips overlap
#>      <char>     <Date>   <int>  <lgcl>
#> 1: spo_gtfs 2019-01-23    7948    TRUE
#> 2: poa_gtfs 2019-01-23     194    TRUE

Overlap periods

When only the dates matter, resolution = "periods" summarises the days on which all feeds have service into periods of consecutive days, with their first and last days (both included) and their length:

get_calendar_overlap(c(spo_path, poa_path), resolution = "periods")
#>    start_date   end_date n_days
#>        <Date>     <Date>  <int>
#> 1: 2019-01-18 2019-04-18     91

The São Paulo and Porto Alegre feeds overlap for 91 days, from January 18 to April 18, 2019. When the feeds don’t overlap, the result has no rows:

ber_path <- system.file("extdata/ber_gtfs.zip", package = "gtfstools")
feeds <- c(spo_path, poa_path, ber_path)

get_calendar_overlap(feeds, resolution = "periods")
#> Empty data.table (0 rows and 3 cols): start_date,end_date,n_days

With plot = TRUE, a timeline shows why. Each feed is a bar spanning its service days, and the periods in which all feeds have service are shaded in the background. The São Paulo feed spans more than 12 years, so the plot is zoomed in:

get_calendar_overlap(feeds, resolution = "periods", plot = TRUE) +
  coord_cartesian(xlim = as.Date(c("2018-12-01", "2021-07-01")))

The Berlin feed starts in November 2020, after the São Paulo feed ends, so no date has service in all three feeds.

Once a date is chosen, the feeds can be combined into a single one with merge_gtfs(), or reduced to the services of that date with the filtering functions (see the filtering vignette).