tidytransit-introduction

Get Headways by Route

This example uses NYC MTA subway schedule data to identify shortest median headways by route, pulling data directly from the MTA’s GTFS URL.

library(tidytransit)
library(dplyr)

# 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 <- import_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 <- import_gtfs(local_gtfs_path, 
                   local=TRUE)

## Unzipped the following files to directory '/var/folders/hk/12t7fl6d08s7r494zd9sb8v40000gn/T//RtmpT1HZG2'...

##  [1] "agency.txt"                                                                          
##  [2] "calendar_dates.txt"                                                                  
##  [3] "calendar.txt"                                                                        
##  [4] "libloc_185_302b971d5ddbcf93.rds"                                                     
##  [5] "libloc_213_722ad5f9e07c7fe1.rds"                                                     
##  [6] "repos_https%3A%2F%2Fcran.rstudio.com%2Fbin%2Fmacosx%2Fel-capitan%2Fcontrib%2F3.5.rds"
##  [7] "routes.txt"                                                                          
##  [8] "rs-graphics-911183dd-d5d7-4595-af33-cbd4bab415df"                                    
##  [9] "shapes.txt"                                                                          
## [10] "stop_times.txt"                                                                      
## [11] "stops.txt"                                                                           
## [12] "transfers.txt"                                                                       
## [13] "trips.txt"

## Reading agency_df

## Reading calendar_dates_df

## Reading calendar_df

## Reading routes_df

## Reading shapes_df

## Reading stop_times_df

## Reading stops_df

## Reading transfers_df

## Reading trips_df

## ...done.

## Testing data structure...

## ...passed. Valid GTFS object.

## Converting stops to simple features

## Converting routes to simple features

# Get route frequencies
nyc_route_freqs <- nyc %>% 
  get_route_frequency()

# Find routes with shortest median headways
nyc_fastest_routes <- nyc_route_freqs %>% 
  filter(median_headways < 25) %>% 
  arrange(median_headways)

knitr::kable(head(nyc_fastest_routes))

Get Headways by Stop

You can also identify shortest headways by stop.

nyc_stop_freqs <- nyc %>% 
  get_stop_frequency(by_route = FALSE) %>%
  inner_join(nyc$stops_df, by = "stop_id") %>%
  select(stop_name, direction_id, stop_id, headway) %>%
  arrange(headway)

head(nyc_stop_freqs)

## # A tibble: 6 x 4
## # Groups:   direction_id, stop_id [6]
##   stop_name             direction_id stop_id headway
##   <chr>                        <int> <chr>     <dbl>
## 1 Times Sq - 42 St                 0 902N       3.60
## 2 Grand Central - 42 St            1 901S       3.60
## 3 Times Sq - 42 St                 1 902S       3.60
## 4 Grand Central - 42 St            0 901N       3.61
## 5 Mets - Willets Point             0 702N       3.72
## 6 Junction Blvd                    0 707N       3.72

## # A tibble: 6 x 4
## # Groups:   direction_id, stop_id [6]
##   direction_id stop_id stop_name             headway
##          <int> <chr>   <chr>                   <dbl>
## 1            0 902N    Times Sq - 42 St         3.60
## 2            1 901S    Grand Central - 42 St    3.60
## 3            1 902S    Times Sq - 42 St         3.60
## 4            0 901N    Grand Central - 42 St    3.61
## 5            0 702N    Mets - Willets Point     3.72
## 6            0 707N    Junction Blvd            3.72

Map Route Frequencies

Perhaps you want to map subway routes and color-code each route by how often trains come.

When you call import_gtfs, tidytransit attempts to append a simple features data frame for stops and routes to the list of gtfs dataframes.

You can join these simple features dataframes to the frequency calculation dataframes above and then plot them.

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

plot(routes_sf_frequencies)

GTFS Table Relationships

Source: Wikimedia, user -stk.