Mapping GTFS feeds

Maps are often the best way to get a sense of a transit network: where its routes run, how fast they are and which stops are the busiest. gtfstools includes a few functions that convert the tables of a GTFS feed into spatial sf objects, which can be easily mapped with {ggplot2}. This vignette will show you how to map a feed using the following functions:

We will start by loading the packages required by this demonstration into the current R session:

library(gtfstools)
library(ggplot2)
library(dplyr)

Mapping the transit network

get_shapes_geometry() converts the shapes table into an sf object with the geometry of each shape. Mapping them gives an overview of the whole network described in the feed. Let’s see how that works using São Paulo’s feed:

path <- system.file("extdata/spo_gtfs.zip", package = "gtfstools")
gtfs <- read_gtfs(path)
shapes <- get_shapes_geometry(gtfs)

ggplot() +
  geom_sf(data = shapes) +
  theme_void()

Mapping routes by speed

Spatial objects become more interesting when combined with other information about the feed. Let’s map the routes by their average speed. First, we calculate summary statistics of the speeds of the trips of each route with get_route_speed(). We use mean_speed, the simple mean of the speeds of the trips of each route (frequency-based trips count once, regardless of their headway):

route_speed <- get_route_speed(gtfs)

head(route_speed)
#>    route_id min_speed mean_speed median_speed max_speed  sd_speed
#>      <char>     <num>      <num>        <num>     <num>     <num>
#> 1:  2002-10  8.360838   8.360838     8.360838  8.360838        NA
#> 2:  2105-10  9.651135   9.951090     9.951090 10.251044 0.4241997
#> 3:  2161-10 11.172445  11.398943    11.398943 11.625440 0.3203161
#> 4:  4491-10 12.011979  13.585046    13.585046 15.158114 2.2246538
#> 5:  5290-10  9.103782   9.870054     9.870054 10.636326 1.0836719
#> 6:  6450-51 11.450417  11.450417    11.450417 11.450417        NA

Then, we join these speeds to the geometries returned by get_route_geometry(), which includes one row for each distinct geometry of a route’s trips (e.g. one for each direction):

# the sf object comes first, so that the result is also an sf object
routes <- get_route_geometry(gtfs) |>
  dplyr::inner_join(route_speed, by = "route_id")

ggplot() +
  geom_sf(data = routes, aes(color = mean_speed)) +
  scale_color_viridis_c(name = "Speed (km/h)") +
  theme_void()

Mapping stops by frequency

Similarly, we can map the stops by the number of departures from each one of them in the morning peak, calculated with get_stop_frequency(). Since the departures are counted by service, we first keep only the services that run on mondays with filter_by_weekday(), and then add up the departures of each stop:

weekday_gtfs <- filter_by_weekday(gtfs, "monday")

stop_frequency <- get_stop_frequency(
  gtfs = weekday_gtfs,
  from = "07:00:00",
  to = "09:00:00"
)

stop_frequency <- stop_frequency |>
  dplyr::group_by(stop_id) |>
  dplyr::summarise(
    departures = sum(departures), 
    .groups = "drop"
    )

get_stops_geometry() converts the stops into points, to which we join the number of departures. The size and the color of each stop vary with its number of departures:

stops <- get_stops_geometry(gtfs, stop_id = stop_frequency$stop_id)

stops <- stops |>
  dplyr::inner_join(stop_frequency, by = "stop_id")

# draws the busiest stops on top
stops <- stops |>
  dplyr::arrange(departures)

ggplot() +
  geom_sf(
    data = stops,
    aes(color = departures, size = departures),
    alpha = 0.7
  ) +
  scale_color_viridis_c(guide = "legend") +
  labs(color = "Departures", size = "Departures") +
  theme_void()

Note:

The speeds and frequencies used above are just two examples of the information that can be extracted from a feed. For more information on these and other functions, please see the querying vignette and the function reference.