In this vignette a departure timetable for a stop is generated and visualised. For some analysis it is important to know how and when a single stop is served and workflows to gather and plot such data can help with this analysis.
We use a feed from the New York Metropolitan Transportation Authority. It is provided as a sample feed with tidytransit but you can read it directly from the MTA’s website.
To display where a bus (or any public transit vehicle) is headed on a
timetable we need the column trip_headsign
in
gtfs$trips
. This is an optional field but our example feed
provides this information. To display where a vehicle comes from on the
timetable we need to create a new column in gtfs$trips
which we’ll call trip_origin
.
# get the id of the first stop in the trip's stop sequence
first_stop_id <- gtfs$stop_times %>%
group_by(trip_id) %>%
summarise(stop_id = stop_id[which.min(stop_sequence)])
# join with the stops table to get the stop_name
first_stop_names <- left_join(first_stop_id, gtfs$stops, by="stop_id")
# rename the first stop_name as trip_origin
trip_origins <- first_stop_names %>% select(trip_id, trip_origin = stop_name)
# join the trip origins back onto the trips
gtfs$trips <- left_join(gtfs$trips, trip_origins, by = "trip_id")
## # A tibble: 6 × 2
## route_id trip_origin
## <chr> <chr>
## 1 1 Van Cortlandt Park - 242 St
## 2 1 Van Cortlandt Park - 242 St
## 3 1 Van Cortlandt Park - 242 St
## 4 1 Van Cortlandt Park - 242 St
## 5 1 South Ferry
## 6 1 Van Cortlandt Park - 242 St
In case trip_headsign
does not exist in the feed it can
be generated similarly to trip_origin
:
if(!exists("trip_headsign", where = gtfs$trips)) {
# get the last id of the trip's stop sequence
trip_headsigns <- gtfs$stop_times %>%
group_by(trip_id) %>%
summarise(stop_id = stop_id[which.max(stop_sequence)]) %>%
left_join(gtfs$stops, by="stop_id") %>%
select(trip_id, trip_headsign.computed = stop_name)
# assign the headsign to the gtfs object
gtfs$trips <- left_join(gtfs$trips, trip_headsigns, by = "trip_id")
}
To create a departure timetable, we first need to find the ids of all
stops in the stops table with the same same name, as
stop_name
might cover different platforms and thus have
multiple stop_ids in the stops table.
Note that multiple unrelated stops can have the same
stop_name
, see cluster_stops()
for examples
how to find these cases.
To the selected stop_ids for Time Square, we can join trip columns:
route_id
, service_id
,
trip_headsign
, and trip_origin
. Because
stop_ids and trips are linked via the stop_times
data
frame, we do this by joining the stop_ids we’ve selected to the
stop_times data frame and then to the trips data frame.
Each trip belongs to a route, and the route short name can be added
to the departures by joining the trips data frame with
gtfs$routes
.
departures <- departures %>%
left_join(gtfs$routes %>%
select(route_id,
route_short_name),
by = "route_id")
Now we have a data frame that tells us about the origin, destination, and time at which each train departs from Times Square for every possible schedule of service.
departures %>%
select(arrival_time,
departure_time,
trip_headsign,trip_origin,
route_id) %>%
head() %>%
knitr::kable()
arrival_time | departure_time | trip_headsign | trip_origin | route_id |
---|---|---|---|---|
01:29:30 | 01:29:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
01:49:30 | 01:49:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
02:09:30 | 02:09:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
02:29:30 | 02:29:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
02:49:30 | 02:49:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
03:09:30 | 03:09:30 | Van Cortlandt Park - 242 St | South Ferry | 1 |
However, we don’t know days on which these trips run. Using the
service_id column on our calculated departures and tidytransit’s
calculated dates_services
data frame, we can filter trips
to a given date of interest.
## # A tibble: 6 × 2
## date service_id
## <date> <chr>
## 1 2018-06-24 ASP18GEN-1037-Sunday-00
## 2 2018-06-24 ASP18GEN-2048-Sunday-00
## 3 2018-06-24 ASP18GEN-3041-Sunday-00
## 4 2018-06-24 ASP18GEN-4049-Sunday-00
## 5 2018-06-24 ASP18GEN-5048-Sunday-00
## 6 2018-06-24 ASP18GEN-6030-Sunday-00
Please see the servicepatterns
vignette for further
examples on how to use this table.
Now we are ready to extract the same service table for any given day of the year.
For example, for August 23rd 2018, a typical weekday, we can filter as follows:
services_on_180823 <- gtfs$.$dates_services %>%
filter(date == "2018-08-23") %>% select(service_id)
departures_180823 <- departures %>%
inner_join(services_on_180823, by = "service_id")
How services and trips are set up depends largely on the feed. For an
idea how to handle other dates and questions about schedules have a look
at the servicepatterns
vignette.
departures_180823 %>%
arrange(departure_time, stop_id, route_short_name) %>%
select(departure_time, stop_id, route_short_name, trip_headsign) %>%
filter(departure_time >= hms::hms(hours = 7)) %>%
filter(departure_time < hms::hms(hours = 7, minutes = 10)) %>%
knitr::kable()
departure_time | stop_id | route_short_name | trip_headsign |
---|---|---|---|
07:00:00 | 725S | 7X | 34 St - 11 Av |
07:00:30 | 902N | S | Times Sq - 42 St |
07:01:00 | 127N | 3 | Harlem - 148 St |
07:01:00 | 127S | 3 | New Lots Av |
07:01:00 | 725N | 7 | Flushing - Main St |
07:01:30 | R16N | Q | 96 St |
07:02:00 | R16S | R | Bay Ridge - 95 St |
07:02:30 | 725S | 7 | 34 St - 11 Av |
07:02:30 | 902S | S | Grand Central - 42 St |
07:03:00 | 725N | 7 | Flushing - Main St |
07:03:30 | 127S | 2 | Flatbush Av - Brooklyn College |
07:04:00 | 127N | 1 | Van Cortlandt Park - 242 St |
07:04:00 | R16S | Q | Coney Island - Stillwell Av |
07:04:30 | 902N | S | Times Sq - 42 St |
07:05:00 | 725S | 7X | 34 St - 11 Av |
07:05:00 | R16S | W | Whitehall St |
07:05:30 | 725N | 7 | Flushing - Main St |
07:06:00 | R16N | R | Forest Hills - 71 Av |
07:06:30 | 127S | 1 | South Ferry |
07:06:30 | 902S | S | Grand Central - 42 St |
07:07:00 | 127N | 2 | Wakefield - 241 St |
07:07:00 | R16S | R | Bay Ridge - 95 St |
07:07:30 | 725S | 7 | 34 St - 11 Av |
07:08:00 | 725N | 7 | Flushing - Main St |
07:08:00 | R16N | N | Astoria - Ditmars Blvd |
07:08:30 | 127S | 3 | New Lots Av |
07:08:30 | 902N | S | Times Sq - 42 St |
07:09:00 | R16S | N | Coney Island - Stillwell Av |
We’ll now plot all departures from Times Square depending on trip_headsign and route. We can use the route colors provided in the feed.
route_colors <- gtfs$routes %>% select(route_id, route_short_name, route_color)
route_colors$route_color[which(route_colors$route_color == "")] <- "454545"
route_colors <- setNames(paste0("#", route_colors$route_color), route_colors$route_short_name)
ggplot(departures_180823) + theme_bw() +
geom_point(aes(y=trip_headsign, x=departure_time, color = route_short_name), size = 0.2) +
scale_x_time(breaks = seq(0, max(as.numeric(departures$departure_time)), 3600),
labels = scales::time_format("%H:%M")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(legend.position = "bottom") +
scale_color_manual(values = route_colors) +
labs(title = "Departures from Times Square on 08/23/18")
Now we plot departures for all stop_ids with the same name, so we can separate for different stop_ids. The following plot shows all departures for stop_ids 127N and 127S from 7 to 8 AM.
departures_180823_sub_7to8 <- departures_180823 %>%
filter(stop_id %in% c("127N", "127S")) %>%
filter(departure_time >= hms::hms(hours = 7) & departure_time <= hms::hms(hours = 8))
ggplot(departures_180823_sub_7to8) + theme_bw() +
geom_point(aes(y=trip_headsign, x=departure_time, color = route_short_name), size = 1) +
scale_x_time(breaks = seq(7*3600, 9*3600, 300), labels = scales::time_format("%H:%M")) +
scale_y_discrete(drop = F) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
theme(legend.position = "bottom") +
labs(title = "Departures from Times Square on 08/23/18") +
facet_wrap(~stop_id, ncol = 1)
Of course this plot idea can be expanded further. You could also differentiate each route by direction (using direction_id, headsign, origin or next/previous stops). Another approach is to calculate frequencies and show different levels of service during the day, all depending on the goal of your analysis.