Author: Mark Padgham
Date: 2025-06-18

# gtfsrouter

#### Mark Padgham

#### 2025-06-18

# 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 ()

    ## /tmp/RtmpOtCvKQ/vbb.zip

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

    ## [1] TRUE

    gtfs <- extract_gtfs (f)

    ## ▶ Unzipping GTFS archive✔ Unzipped GTFS archive
    ## ▶ Extracting GTFS feed✔ Extracted GTFS feed
    ## ▶ Converting stop times to seconds✔ Converted stop times to seconds
    ## ▶ Converting transfer times to seconds✔ Converted transfer times to seconds

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
    )

    ## Day not specified; extracting timetable for wednesday

route_name | trip_name | stop_name | arrival_time | departure_time
--- | --- | --- | --- | ---
U4 | U Nollendorfplatz | S+U Innsbrucker Platz (Berlin) | 12:06:00 | 12:06:00
U4 | U Nollendorfplatz | U Rathaus Schoneberg (Berlin) | 12:07:00 | 12:07:00
U4 | U Nollendorfplatz | U Bayerischer Platz (Berlin) | 12:08:30 | 12:08:30
U4 | U Nollendorfplatz | U Viktoria-Luise-Platz (Berlin) | 12:10:00 | 12:10:00
U4 | U Nollendorfplatz | U Nollendorfplatz (Berlin) | 12:12:00 | 12:12:00
U2 | S+U Pankow | U Nollendorfplatz (Berlin) | 12:17:00 | 12:17:00
U2 | S+U Pankow | U Bulowstr. (Berlin) | 12:18:30 | 12:18:30
U2 | S+U Pankow | U Gleisdreieck (Berlin) | 12:20:30 | 12:20:30
U2 | S+U Pankow | U Mendelssohn-Bartholdy-Park (Berlin) | 12:22:00 | 12:22:00
U2 | S+U Pankow | S+U Potsdamer Platz (Bln) [U2] | 12:23:30 | 12:23:30
U2 | S+U Pankow | U Mohrenstr. (Berlin) | 12:25:00 | 12:25:00
U2 | S+U Pankow | Berlin, U Stadtmitte U2 | 12:26:00 | 12:26:00
U2 | S+U Pankow | U Hausvogteiplatz (Berlin) | 12:27:30 | 12:27:30
U2 | S+U Pankow | U Spittelmarkt (Berlin) | 12:29:00 | 12:29:00
U2 | S+U Pankow | U Markisches Museum (Berlin) | 12:30:00 | 12:30:00
U2 | S+U Pankow | U Klosterstr. (Berlin) | 12:31:30 | 12:31:30
U2 | S+U Pankow | S+U Alexanderplatz (Berlin) [U2] | 12:33:30 | 12:33:30

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)

    ## ▶ Unzipping GTFS archive✔ Unzipped GTFS archive
    ## ▶ Extracting GTFS feed✔ Extracted GTFS feed
    ## ▶ Converting stop times to seconds✔ Converted stop times to seconds
    ## ▶ Converting transfer times to seconds✔ Converted transfer times to seconds

    from <- "Innsbrucker Platz"
    to <- "Alexanderplatz"
    system.time (
        gtfs_route (
            gtfs,
            from = from,
            to = to,
            start_time = "12:00:00",
            day = "Sunday"
        )
    )

    ##    user  system elapsed
    ##   0.054   0.000   0.054

    names (gtfs)

    ## [1] "calendar"   "routes"     "trips"      "stop_times" "stops"
    ## [6] "transfers"

    # explicit pre-processing to extract timetable for Sunday
    gtfs <- gtfs_timetable (gtfs,
        day = "Sunday"
    )
    names (gtfs)

    ## [1] "calendar"   "routes"     "trips"      "stop_times" "stops"
    ## [6] "transfers"  "timetable"  "stop_ids"   "trip_ids"

    system.time (gtfs_route (
        gtfs,
        from = from,
        to = to,
        start_time = "12:00:00"
    ))

    ##    user  system elapsed
    ##   0.042   0.000   0.043

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)

route_id | agency_id | route_short_name | route_long_name | route_type | route_color | route_text_color | route_desc
--- | --- | --- | --- | --- | --- | --- | ---
10141_109 | 1 | S1 |  | 109 | E64DFF | FFFFFF |
10142_109 | 1 | S1 |  | 109 | E64DFF | FFFFFF |
10143_109 | 1 | S2 |  | 109 | 00B300 | FFFFFF |
10144_109 | 1 | S2 |  | 109 | 00B300 | FFFFFF |
10145_109 | 1 | S25 |  | 109 |  |  |
10148_109 | 1 | S3 |  | 109 |  |  |

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 Innsbrucker Platz to Alexanderplatz 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"
    )

route_name | trip_name | stop_name | arrival_time | departure_time
--- | --- | --- | --- | ---
S42 | S Sudkreuz Bhf | S+U Innsbrucker Platz (Berlin) | 12:06:42 | 12:07:12
S42 | S Sudkreuz Bhf | S Schoneberg (Berlin) | 12:08:18 | 12:08:48
S42 | S Sudkreuz Bhf | S Sudkreuz Bhf (Berlin) | 12:10:12 | 12:10:12
S2 | S Buch | S Sudkreuz Bhf (Berlin) | 12:16:18 | 12:16:54
S2 | S Buch | S+U Yorckstr. S2 S25 S26 U7 (Berlin) | 12:19:00 | 12:19:30
S2 | S Buch | S Anhalter Bahnhof (Berlin) | 12:21:42 | 12:22:12
S2 | S Buch | S+U Potsdamer Platz Bhf (Berlin) | 12:23:48 | 12:24:18
S2 | S Buch | S+U Brandenburger Tor (Berlin) | 12:25:54 | 12:26:24
S2 | S Buch | S+U Friedrichstr. Bhf (Berlin) | 12:27:36 | 12:28:24
S7 | S Ahrensfelde Bhf | S+U Friedrichstr. Bhf (Berlin) | 12:30:36 | 12:31:24
S7 | S Ahrensfelde Bhf | S Hackescher Markt (Berlin) | 12:32:54 | 12:33:24
S7 | S Ahrensfelde Bhf | S+U Alexanderplatz Bhf (Berlin) | 12:34:36 | 12:35:24

## 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
    )

route_name | trip_name | stop_name | arrival_time | departure_time
--- | --- | --- | --- | ---
S7 | S Potsdam Hauptbahnhof | S+U Alexanderplatz Bhf (Berlin) | 11:59:54 | 12:00:42
S7 | S Potsdam Hauptbahnhof | S Hackescher Markt (Berlin) | 12:01:54 | 12:02:24
S7 | S Potsdam Hauptbahnhof | S+U Friedrichstr. Bhf (Berlin) | 12:03:54 | 12:04:42
S2 | S Buch | S+U Friedrichstr. Bhf (Berlin) | 12:07:36 | 12:08:24
S2 | S Buch | S Oranienburger Str. (Berlin) | 12:09:42 | 12:10:12
S2 | S Buch | S Nordbahnhof (Berlin) | 12:11:42 | 12:12:12
S2 | S Buch | S Humboldthain (Berlin) | 12:14:24 | 12:14:54
S2 | S Buch | S+U Gesundbrunnen Bhf (Berlin) | 12:16:12 | 12:16:54
S2 | S Buch | S Bornholmer Str. (Berlin) | 12:18:24 | 12:18:54
S2 | S Buch | S+U Pankow (Berlin) | 12:20:42 | 12:21:18

And the earliest-departing route arrives at Pankow at 12:20:42, departing Alexanderplatz at 12:00:42. In contrast, the earliest-arriving service is:

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

route_name | trip_name | stop_name | arrival_time | departure_time
--- | --- | --- | --- | ---
U2 | S+U Pankow | S+U Alexanderplatz (Berlin) [U2] | 12:09:00 | 12:09:00
U2 | S+U Pankow | U Rosa-Luxemburg-Platz (Berlin) | 12:11:00 | 12:11:00
U2 | S+U Pankow | U Senefelderplatz (Berlin) | 12:12:30 | 12:12:30
U2 | S+U Pankow | U Eberswalder Str. (Berlin) | 12:14:30 | 12:14:30
U2 | S+U Pankow | S+U Schonhauser Allee (Berlin) | 12:16:30 | 12:16:30
U2 | S+U Pankow | U Vinetastr. (Berlin) | 12:19:00 | 12:19:00
U2 | S+U Pankow | S+U Pankow (Berlin) | 12:20:30 | 12:20:30

This service departs 8min, 18s later at 12:09:00, and arrives 12 seconds earlier at 12:20:30. The earliest-arriving service thus entails 8min, 30s 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 ()

    ##    route_name              trip_name                            stop_name
    ## 1          S7 S Potsdam Hauptbahnhof      S+U Alexanderplatz Bhf (Berlin)
    ## 2          S7 S Potsdam Hauptbahnhof          S Hackescher Markt (Berlin)
    ## 3          S7 S Potsdam Hauptbahnhof       S+U Friedrichstr. Bhf (Berlin)
    ## 4          S2          S Lichtenrade       S+U Friedrichstr. Bhf (Berlin)
    ## 5          S2          S Lichtenrade       S+U Brandenburger Tor (Berlin)
    ## 6          S2          S Lichtenrade     S+U Potsdamer Platz Bhf (Berlin)
    ## 7          S2          S Lichtenrade          S Anhalter Bahnhof (Berlin)
    ## 8          S2          S Lichtenrade S+U Yorckstr. S2 S25 S26 U7 (Berlin)
    ## 9          S2          S Lichtenrade              S Sudkreuz Bhf (Berlin)
    ## 10        S46              S Westend              S Sudkreuz Bhf (Berlin)
    ## 11        S46              S Westend                S Schoneberg (Berlin)
    ## 12        S46              S Westend       S+U Innsbrucker Platz (Berlin)
    ##    arrival_time departure_time
    ## 1      12:19:54       12:20:42
    ## 2      12:21:54       12:22:24
    ## 3      12:23:54       12:24:42
    ## 4      12:28:00       12:28:42
    ## 5      12:29:54       12:30:24
    ## 6      12:31:54       12:32:24
    ## 7      12:34:00       12:34:30
    ## 8      12:36:42       12:37:12
    ## 9      12:39:18       12:39:54
    ## 10     12:43:00       12:43:42
    ## 11     12:45:12       12:45:42
    ## 12     12:46:54       12:47:24

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:

    gtfs_home = "<my home station>"
    gtfs_work = "<my work station>"
    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:

    go_home (wait = 1)

    ##    route_name       trip_name                        stop_name arrival_time
    ## 1          U8 S+U Hermannstr. S+U Alexanderplatz (Berlin) [U8]     12:23:30
    ## 2          U8 S+U Hermannstr.     S+U Jannowitzbrucke (Berlin)     12:25:00
    ## 3          U8 S+U Hermannstr.   U Heinrich-Heine-Str. (Berlin)     12:26:30
    ## 4          U8 S+U Hermannstr.           U Moritzplatz (Berlin)     12:28:00
    ## 5          U8 S+U Hermannstr.        U Kottbusser Tor (Berlin)     12:30:00
    ## 6          U8 S+U Hermannstr.         U Schonleinstr. (Berlin)     12:31:30
    ## 7          U8 S+U Hermannstr.          U Hermannplatz (Berlin)     12:33:30
    ## 8          U8 S+U Hermannstr.            U Boddinstr. (Berlin)     12:35:00
    ## 9          U8 S+U Hermannstr.             U Leinestr. (Berlin)     12:36:30
    ## 10         U8 S+U Hermannstr.         S+U Hermannstr. (Berlin)     12:37:30
    ## 11        S41  S Sudkreuz Bhf         S+U Hermannstr. (Berlin)     12:39:24
    ## 12        S41  S Sudkreuz Bhf           S+U Tempelhof (Berlin)     12:43:12
    ## 13        S41  S Sudkreuz Bhf          S Sudkreuz Bhf (Berlin)     12:46:12
    ## 14        S41  S Sudkreuz Bhf            S Schoneberg (Berlin)     12:47:42
    ## 15        S41  S Sudkreuz Bhf   S+U Innsbrucker Platz (Berlin)     12:49:24
    ##    departure_time
    ## 1        12:23:30
    ## 2        12:25:00
    ## 3        12:26:30
    ## 4        12:28:00
    ## 5        12:30:00
    ## 6        12:31:30
    ## 7        12:33:30
    ## 8        12:35:00
    ## 9        12:36:30
    ## 10       12:37:30
    ## 11       12:39:54
    ## 12       12:43:42
    ## 13       12:46:12
    ## 14       12:48:12
    ## 15       12:49:54

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