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.
First, we load {gtfsrealtime} to read GTFS-realtime files and {dplyr} to inspect and summarize the resulting data frame.
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.
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 id may appear in multiple
rows when the feed contains stop-level updates for multiple stops. Note
that the id uniquely identifies a single trip update; the
trip_id field is not deduplicated so that duplicate records
can still be joined to static GTFS.
glimpse(updates)
#> Rows: 90,881
#> Columns: 27
#> $ 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, …
#> $ feed_timestamp <dttm> 2026-01-28 17:13:34, 2026-01-28 17:13:3…
#> $ feed_version <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ file_index <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…Because a single trip can include predictions for multiple stops, it
is useful to inspect all rows associated with one 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(id == first(id)) |>
select(
id,
trip_id,
route_id,
stop_id,
stop_sequence,
arrival_time,
departure_time,
arrival_delay,
departure_delay
)
#> id trip_id route_id
#> 1 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 2 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 3 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 4 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 5 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 6 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 7 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 8 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 9 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> 10 MV_A6-Weekday-SDon-102600_M96_826 MV_A6-Weekday-SDon-102600_M96_826 M96
#> stop_id stop_sequence arrival_time departure_time arrival_delay
#> 1 401933 1 2026-01-28 17:13:20 2026-01-28 17:13:20 NA
#> 2 401935 3 2026-01-28 17:14:15 2026-01-28 17:14:15 NA
#> 3 401936 4 2026-01-28 17:15:58 2026-01-28 17:15:58 NA
#> 4 401937 5 2026-01-28 17:17:42 2026-01-28 17:17:42 NA
#> 5 404087 6 2026-01-28 17:21:41 2026-01-28 17:21:41 NA
#> 6 401939 7 2026-01-28 17:24:30 2026-01-28 17:24:30 NA
#> 7 401941 8 2026-01-28 17:27:43 2026-01-28 17:27:43 NA
#> 8 401942 9 2026-01-28 17:30:05 2026-01-28 17:30:05 NA
#> 9 401943 10 2026-01-28 17:32:03 2026-01-28 17:32:03 NA
#> 10 903003 11 2026-01-28 17:33:38 2026-01-28 17:33:38 NA
#> departure_delay
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 NA
#> 10 NA