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/RtmpOKQwWs/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

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"
)

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 
##   0.041   0.000   0.041

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 
##   0.027   0.004   0.032

head (gtfs$route)

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

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

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

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.