This vignette shows how to read GTFS-realtime trip updates into R using the {gtfsrealtime} package. Trip updates describe the real-time progress of a vehicle along a scheduled trip, including predicted arrival and departure times, delays, skipped stops, canceled trips, and other real-time information. The {gtfsrealtime} package reads the nested GTFS-realtime trip update format and flattens it into a data frame that is easier to inspect and analyze in R.
Load libraries
First, we load {gtfsrealtime} to read GTFS-realtime files and {dplyr} to inspect and summarize the resulting data frame.
Load a GTFS-realtime trip updates feed
This example uses a New York City trip updates feed included with
{gtfsrealtime}. The file is compressed with bzip2 to save space.
{gtfsrealtime} can automatically detect and read uncompressed files as
well as files compressed with zip, gzip, or bzip2. Zip files can contain
multiple GTFS-realtime files, in which case {gtfsrealtime} will read all
of them. You can differentiate which file each update came from based on
the file_index field.
GTFS-realtime time values are stored as Unix timestamps, which are interpreted relative to UTC. To convert to local time, we provide a local time zone. Time zones are specified in standardized TZ database format, generally Continent/City. If you do not want to convert times, you can specify a time zone of Etc/UTC.
updates <- read_gtfsrt_trip_updates(
system.file("nyc-trip-updates.pb.bz2", package = "gtfsrealtime"),
"America/New_York"
)When reading this example feed, {gtfsrealtime} warns that some
GTFS-realtime entity IDs are duplicated. In these cases, the package
appends suffixes such as _duplicated_1 so that each row can
be represented with a unique id. There are quite a few of
them, so they are suppressed here to keep the vignette readable, but the
first two are:
1: ! ID UP_A6-Weekday-SDon-094800_B6_243 is duplicated. Replacing with UP_A6-Weekday-SDon-094800_B6_243_duplicated_1 . This may cause joins between different
GTFS-realtime files (even within a ZIP archive) to be incorrect.
2: ! ID UP_A6-Weekday-SDon-094800_B6_243 is duplicated. Replacing with UP_A6-Weekday-SDon-094800_B6_243_duplicated_2 . This may cause joins between different
GTFS-realtime files (even within a ZIP archive) to be incorrect.These warnings are useful in practice: duplicated entity IDs can affect workflows that join records across GTFS-realtime files or across multiple files within a ZIP archive, as IDs may no longer match across files.
Explore trip updates
GTFS-realtime trip updates are hierarchical;
one trip update can contain information about the trip as a whole as
well as updates for multiple stops along that trip.
read_gtfsrt_trip_updates() flattens that structure into a
data frame. As a result, the same trip_id may appear in
multiple rows when the feed contains stop-level updates for multiple
stops.
glimpse(updates)
#> Rows: 90,881
#> Columns: 26
#> $ id <chr> "MV_A6-Weekday-SDon-102600_M96_826", "MV…
#> $ trip_id <chr> "MV_A6-Weekday-SDon-102600_M96_826", "MV…
#> $ route_id <chr> "M96", "M96", "M96", "M96", "M96", "M96"…
#> $ direction_id <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1…
#> $ start_time <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ start_date <chr> "20260128", "20260128", "20260128", "202…
#> $ trip_schedule_relationship <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ modifications_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ vehicle_id <chr> "MTA NYCT_9771", "MTA NYCT_9771", "MTA N…
#> $ vehicle_label <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ vehicle_license_plate <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ vehicle_wheelchair_accessible <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ stop_sequence <dbl> 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 1, 2, 3,…
#> $ stop_id <chr> "401933", "401935", "401936", "401937", …
#> $ arrival_delay <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ arrival_time <dttm> 2026-01-28 17:13:20, 2026-01-28 17:14:1…
#> $ arrival_scheduled_time <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ arrival_uncertainty <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ departure_delay <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ departure_time <dttm> 2026-01-28 17:13:20, 2026-01-28 17:14:1…
#> $ departure_scheduled_time <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ departure_uncertainty <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ departure_occupancy_status <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ stop_schedule_relationship <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ file_timestamp <dttm> 2026-01-28 17:13:34, 2026-01-28 17:13:3…
#> $ file_index <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…Inspecting one trip across its stops
Because a single trip can include predictions for multiple stops, it
is useful to inspect all rows associated with one trip_id.
In the example below, we select the first trip in the feed and display
the route, stop sequence, stop ID, and predicted arrival and departure
times for each stop. If a trip update has no stop time updates, it will
appear as a single row with all the stop_* fields NA.
Documentation for all of the columns is in the documentation for
read_gtfsrt_trip_updates().
updates |>
filter(trip_id == first(trip_id)) |>
select(
trip_id,
route_id,
stop_id,
stop_sequence,
arrival_time,
departure_time,
arrival_delay,
departure_delay
)
#> trip_id route_id stop_id stop_sequence
#> 1 MV_A6-Weekday-SDon-102600_M96_826 M96 401933 1
#> 2 MV_A6-Weekday-SDon-102600_M96_826 M96 401935 3
#> 3 MV_A6-Weekday-SDon-102600_M96_826 M96 401936 4
#> 4 MV_A6-Weekday-SDon-102600_M96_826 M96 401937 5
#> 5 MV_A6-Weekday-SDon-102600_M96_826 M96 404087 6
#> 6 MV_A6-Weekday-SDon-102600_M96_826 M96 401939 7
#> 7 MV_A6-Weekday-SDon-102600_M96_826 M96 401941 8
#> 8 MV_A6-Weekday-SDon-102600_M96_826 M96 401942 9
#> 9 MV_A6-Weekday-SDon-102600_M96_826 M96 401943 10
#> 10 MV_A6-Weekday-SDon-102600_M96_826 M96 903003 11
#> arrival_time departure_time arrival_delay departure_delay
#> 1 2026-01-28 17:13:20 2026-01-28 17:13:20 NA NA
#> 2 2026-01-28 17:14:15 2026-01-28 17:14:15 NA NA
#> 3 2026-01-28 17:15:58 2026-01-28 17:15:58 NA NA
#> 4 2026-01-28 17:17:42 2026-01-28 17:17:42 NA NA
#> 5 2026-01-28 17:21:41 2026-01-28 17:21:41 NA NA
#> 6 2026-01-28 17:24:30 2026-01-28 17:24:30 NA NA
#> 7 2026-01-28 17:27:43 2026-01-28 17:27:43 NA NA
#> 8 2026-01-28 17:30:05 2026-01-28 17:30:05 NA NA
#> 9 2026-01-28 17:32:03 2026-01-28 17:32:03 NA NA
#> 10 2026-01-28 17:33:38 2026-01-28 17:33:38 NA NA