GTFS Tables

Simple Features

• routes_sf
• stops_sf

Frequencies/Headways

• routes_frequencies_df
• stops_frequencies_df

tidytransit prints a message regarding these tables on reading any GTFS file.

# Read in GTFS feed
# here we use a feed included in the package, but note that you can read directly from the New York City Metropolitan Transit Authority using the following URL:
# nyc <- read_gtfs("http://web.mta.info/developers/data/nyct/subway/google_transit.zip")

local_gtfs_path <- system.file("extdata", 
                               "google_transit_nyc_subway.zip", 
                               package = "tidytransit")
nyc <- read_gtfs(local_gtfs_path, 
                 local=TRUE,
                 geometry=TRUE,
                 frequency=TRUE)
#> Calculating route and stop headways using defaults (6 am to 10 pm for weekday service).

Route Frequencies to Routes

For example, joining the standard routes table, with the ‘route_shortname’ variable to routes_frequencies_df.

routes_df_frequencies <- nyc$routes_df %>% 
  inner_join(nyc$routes_frequency_df, by = "route_id") %>% 
          select(route_long_name,
                 median_headways, 
                 mean_headways, 
                 st_dev_headways, 
                 stop_count)
head(routes_df_frequencies)
#> # A tibble: 6 x 5
#>   route_long_name median_headways mean_headways st_dev_headways stop_count
#>   <chr>                     <int>         <int>           <dbl>      <int>
#> 1 Broadway - 7 A…               5             5            0.14         76
#> 2 7 Avenue Expre…               8            36           63.7         118
#> 3 7 Avenue Expre…               8             8            0.06         68
#> 4 Lexington Aven…               7           197          350.           75
#> 5 Lexington Aven…              10            97          245.          100
#> 6 Lexington Aven…              48            48            0            29

You can do the same with ‘simple features tables’.

For example, under the hood, plot(gtfs_obj) is doing this:

routes_sf_frequencies <- nyc$routes_sf %>% 
      inner_join(nyc$routes_frequency_df, by = "route_id") %>% 
          select(median_headways, 
                 mean_headways, 
                 st_dev_headways, 
                 stop_count)
plot(routes_sf_frequencies)

Headways at Stops for a Route

A more complex example of cross-table joins is to pull the stops and their headways for a given route.

This simple question is a great way to begin to understand a lot about the GTFS data model.

First, we’ll need to find a ‘service_id’, which will tell us which stops a route passes through on a given day of the week and year.

When calculating frequencies, tidytransit tries to guess which service_id is representative of a standard weekday by walking through a set of steps. Below we’ll just do some of this manually.

First, lets look at the calendar_df.

head(sample_n(nyc$calendar_df,10))
#> # A tibble: 6 x 10
#>   service_id      monday tuesday wednesday thursday friday saturday sunday
#>   <chr>            <int>   <int>     <int>    <int>  <int>    <int>  <int>
#> 1 ASP18GEN-4097-…      1       1         1        1      1        0      0
#> 2 BSP18GEN-FS011…      1       1         1        1      1        0      0
#> 3 ASP18GEN-1087-…      1       1         1        1      1        0      0
#> 4 ASP18GEN-1037-…      0       0         0        0      0        0      1
#> 5 SIR-SP2018-SI0…      0       0         0        0      0        1      0
#> 6 BSP18GEN-N054-…      0       0         0        0      0        1      0
#> # ... with 2 more variables: start_date <date>, end_date <date>

Then we’ll pull a random route_id and set of service_ids that run on Mondays.

select_service_id <- filter(nyc$calendar_df,monday==1) %>% pull(service_id)
select_route_id <- sample_n(nyc$routes_df,1) %>% pull(route_id)

Now we’ll filter down through the data model to just stops for that route and service_ids.

some_trips <- nyc$trips_df %>%
  filter(route_id %in% select_route_id & service_id %in% select_service_id)

some_stop_times <- nyc$stop_times_df %>% 
  filter(trip_id %in% some_trips$trip_id) 

some_stops <- nyc$stops_sf %>%
  filter(stop_id %in% some_stop_times$stop_id)

Before we plot them, lets pull the frequency calculations from the calculated table onto their geometries.

some_stops_freq_sf <- some_stops %>%
  left_join(nyc$stops_frequency_df, by="stop_id") %>%
  select(headway)
plot(some_stops_freq_sf)

We may–in fact, we probably will–see some surprising outliers for headway calculations in this plot.

Calculating headways at stops is tricky for a number of reasons. One reason is that these calculations can be determined by schedules of service.

But hopefully now you have a better understanding of how you can use the GTFS data model to communicate about these questions.