In ATFutures/gtfs-router: Routing with 'GTFS' (General Transit Feed Specification) Data

library (gtfsrouter)

# Necessary for CRAN to avoid CPU / elapsed time ratios being too high
nthr <- data.table::setDTthreads (1)

1 Background: GTFS and other R packages

GTFS - General Transit Feed Specification - began life in 2005 as the "Google Transit Feed Specification," and was renamed to "General" in 2009. It provides a standardised scheme for representing data on public transport services, routes, frequencies, and timetables. A GTFS data set consists of several comma-delimited (.csv) files detailing routes, stops, trips, transfers, and other aspects, all bundled in a single .zip-compressed archive file. For full details, see the relevant google developer site.

There are currently two other R packages which handle GTFS data:

1. gtfsr, hosted by rOpenSci, developed by Danton Noriega, but no longer under active development.
2. tidytransit, which began as a fork of gtfsr, and currently represents its successor. This package can be used to, "map transit stops and routes, calculate transit frequencies, and validate transit feeds [as well as to read] the General Transit Feed Specification into tidyverse and simple features dataframes."

The one thing neither of these packages enable is the use of GTFS data for transit routing. The gtfsrouter package enables both one-to-one and one-to-many routing. Functionality is demonstrated here through the sample data set included with the package, provided by the "Verkehrsverbund Berlin-Brandenburg" (VBB; or Transport Network Berlin-Brandenburg). The berlin_gtfs data represents a reduced version of the full GTFS data, containing only six tables, and a timetable reduced to the single hour between 12:00-13:00. Like all GTFS software including tidytransit, this package is designed to work directly with GTFS data in .zip-archived format, and so includes a helper function, berlin_gtfs_to_zip(), which exports the internal data set to a locally-stored .zip archive in the tempdir() of the current R session. These data can be exported and re-imported with:

berlin_gtfs_to_zip ()
f <- file.path (tempdir (), "vbb.zip")
file.exists (f)
gtfs <- extract_gtfs (f)

That simply re-creates the original package data, berlin_gtfs (although the extracted data differ through having a couple of additional attributes defining it as a gtfs object).

2. Routing

The primary routing function is gtfs_route(), the example of which uses the gtfs data for the VBB created as described above. In simplest form, routing requires a start and end point, defaulting to the current time as desired start time, and routing for the current day of the week.

from <- "Innsbrucker Platz"
to <- "Alexanderplatz"
gtfs_route (
    gtfs,
    from = from,
    to = to
)

from <- "Innsbrucker Platz"
to <- "Alexanderplatz"
knitr::kable (gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00"
))

Both the start time and day of the week can be explicitly specified:

route <- gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday"
)

2.1 GTFS Timetables

The gtfsrouter package uses the Connection Scan Algorithm, which requires converting the "stop_times" table to a column-wise timetable. The "stop_times" table has row-wise entries for each distinct "trip_id", with consecutive rows for a given value of "trip_id" holding sequential values for stops and associated times (and potentially additional variables). In contrast, the timetables processed by this package have separate columns for departure and arrival stations and times. All routing queries pre-process the original GTFS data with the gtfs_timetable() function, which appends this timetable data, along with two single-column tables of stop and trip ID values. (The timetable itself contains strictly integer values for stops and trips, which are indices into these latter tables.)

The only important point of that from a user's perspective is that routing queries will be faster if this pre-processing step is explicitly implemented with gtfs_timetable() prior to calling gtfs_route(). This is easy to demonstrate using the sample data:

gtfs <- extract_gtfs (f)
from <- "Innsbrucker Platz"
to <- "Alexanderplatz"
system.time (
    gtfs_route (
        gtfs,
        from = from,
        to = to,
        start_time = "12:00:00",
        day = "Sunday"
    )
)
names (gtfs)
# explicit pre-processing to extract timetable for Sunday
gtfs <- gtfs_timetable (gtfs,
    day = "Sunday"
)
names (gtfs)
system.time (gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00"
))

Note that the day parameter is used to extract the timetable, after which it is no longer required in the actual call to gtfs_route().

2.2. Routing by mode of transport

It is also possible to filter by desired mode of transport. This is done by matching the pattern to those given in the route_short_name column of the gtfs$route table:

head (gtfs$route)

knitr::kable (head (gtfs$route))

These short names will differ for each GTFS, with the two primary train systems in Berlin being the underground trains denoted "U" (although not always travelling underground), and street-level trains denoted "S". The default route from r from to r to above was via two "U" services. We can also specify that we'd prefer to travel by "S" services, noting that the route_pattern = "S" specifies a route_short_name that starts with ("^") "S":

gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday",
    route_pattern = "^S"
)

knitr::kable (gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday",
    route_pattern = "^S"
))

2.3. Routing for earliest arrivals or earliest departures

The above route with the "S" services leaves one minute later, and arrives two minutes later. Importantly, gtfs_route() searches by default for the service which arrives at the nominated destination station at the earliest time. This may not always be the first available service departing from the nominated start station. Routing with the earliest departing service, instead of the earliest arriving service, can be specified with the binary earliest_arrival parameter:

from <- "Alexanderplatz"
to <- "Pankow"
gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday",
    earliest_arrival = FALSE
)

from <- "Alexanderplatz"
to <- "Pankow"
r1 <- gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday"
)
r2 <- gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday",
    earliest_arrival = FALSE
)
knitr::kable (r2)

And the earliest-departing route arrives at r to at r r2$arrival_time [nrow (r2)], departing r from at r r2$departure_time [1]. In contrast, the earliest-arriving service is:

gtfs_route (
    gtfs,
    from = from,
    to = to,
    start_time = "12:00:00",
    day = "Sunday",
    earliest_arrival = TRUE
)

knitr::kable (r1)
diff_arrival <- gtfsrouter:::convert_time (r2$arrival_time [nrow (r2)]) -
    gtfsrouter:::convert_time (r1$arrival_time [nrow (r1)])
diff_depart <- gtfsrouter:::convert_time (r1$departure_time [1]) -
    gtfsrouter:::convert_time (r2$departure_time [1])
mm <- floor (diff_depart / 60)
ss <- diff_depart - mm * 60
diff_depart <- paste0 (mm, "min, ", ss, "s")

diff_total <- (gtfsrouter:::convert_time (r2$arrival_time [nrow (r2)]) -
    gtfsrouter:::convert_time (r2$departure_time [1])) -
    (gtfsrouter:::convert_time (r1$arrival_time [nrow (r1)]) -
        gtfsrouter:::convert_time (r1$departure_time [1]))
mm <- floor (diff_total / 60)
ss <- diff_total - mm * 60
diff_total <- paste0 (mm, "min, ", ss, "s")

This service departs r diff_depart later at r r1$departure_time [1], and arrives r diff_arrival seconds earlier at r r1$arrival_time [nrow (r1)]. The earliest-arriving service thus entails r diff_total less travel time than the earliest departing service. It is nevertheless important to note that queries for earliest-arriving services require two full routing runs, whereas earliest-departing services can be executed in a single run. This, bulk queries for analytic purposes will generally be up to twice as first with earliest_arrival = FALSE.

3. Convenience Functions: go_home() and go_to_work()

The gtfsrouter package is intended both to enable statistical analyses of GTFS data sets, as well as for personal, pragmatic purposes. In the latter regard, the package provides two "convenience" functions to allow single-call queries for next available services to "home" and "work" stations. These functions require some initial set-up through specifying environmental variables, but once done can be executed as single calls from any R session to return the next available service.

go_home ()

Sys.setenv ("gtfs_home" = "Innsbrucker Platz")
Sys.setenv ("gtfs_work" = "Alexanderplatz")
Sys.setenv ("gtfs_data" = file.path (tempdir (), "vbb.zip"))
data.table::setDTthreads (1) # See ?setDTthreads: setenv resets it
process_gtfs_local () # If not already done
go_home (start_time = "12:20") # next available service

The complementary function, go_to_work() routes in the reverse direction. These functions are intended to allow real-time queries of public transport schedules from within the comfort of an R session, and will generally be much quicker -- and hopefully easier -- than the arguably burdensome necessity of switching attention from productive R programming to the usual app or website otherwise needed to answer the simple question of when I ought to leave today?

Successfully calling that function requires setting three environmental variables:

Sys.setenv ("gtfs_home" = "<my home station>")
Sys.setenv ("gtfs_work" = "<my work station>")
Sys.setenv ("gtfs_data" = "/full/path/to/gtfs.zip")

along with execution of the single command:

process_gtfs_local ()

This command attempts to reduce the size of the locally-stored GTFS data to the minimum required for local routing, and saves the result as an internal .Rds object in the same location as the gtfs_data environmental variable. Having done that, go_home() will search for the next available service from the nominated work station to the nominated home station, while go_to_work() will search for connections in the other direction.

An even easier way to use these functions is to automatically load those environmental variables at the start of each R session, which can be achieved simply by creating a file named .Renviron in the user's root directory (or opening if it already exists), and pasting or appending the definitions to that file - in this case, without the R-specific Sys.setenv() calls: ```{bash Renviron, eval = FALSE} gtfs_home = "" gtfs_work = "" gtfs_data = "/full/path/to/gtfs.zip"

Of course, this function will only route using locally-stored data, so it is up
to the user to ensure their local copy of `gtfs.zip` is kept up to date.

The functions include one additional feature. Having found the next service with
`go_home()`, I may suspect that I can keep working until the following service.
The simple parameter `wait` enables searching for that following service:
```r
go_home (wait = 1)

go_home (start_time = "12:20", wait = 1)

The service after that can be retrieved with go_home (wait = 2), and so on.

data.table::setDTthreads (nthr)

ATFutures/gtfs-router documentation built on Feb. 20, 2025, 4:11 a.m.