##### Development version 1.2.2 (snapshot: 2026-10-19)
##### CRAN version 1.2.1 (published: 2026-08-21)
GTFSwizard is a set of tools for creating, exploring, and manipulating General Transit Feed Specification (GTFS) files in R.
Its main purpose is to give researchers and practitioners a straightforward way to explore and simulate changes within GTFS files, which represent public transportation schedules and geographic data. The package lets users filter data by routes, trips, stops, and time, generate spatial visualizations, and analyze transit networks, including headways, dwell times, route frequencies, travel times, corridors, and hubs. Functions are also available to delay, change the speed of, split, and merge trips and feeds. This is ongoing work, with new features planned.
The development version is 1.2.2. The CRAN version is 1.2.1.
# CRAN version:
install.packages("GTFSwizard")
library(GTFSwizard)
# Development version:
install.packages('remotes') # if not already installed
remotes::install_github('OPATP/GTFSwizard@main')
library(GTFSwizard)
The package website organizes the documentation by workflow:
explore_gtfs().Use read_gtfs() to read an existing feed, as_wizardgtfs() to convert a GTFS list, and create_gtfs() to build and validate a feed from data frames. Each function returns a wizardgtfs object.
read_gtfs() also accepts UTF-16LE and UTF-16BE text files, converting them
to UTF-8 in a temporary copy without changing the original archive. Set
quiet = FALSE to see a warning listing the converted files.
For a specification and best-practice report, validate_gtfs() runs the
MobilityData Canonical GTFS Schedule Validator.
Install Java 17 or newer and jsonlite. Set download_validator = TRUE to
download and cache the validator CLI JAR
(also requires openssl), or supply an existing JAR path. No download occurs
without this explicit option. The cached JAR is checked against the published
SHA-256 before use. Pass the original
ZIP to audit it before import; an object is exported before
validation, so earlier repairs are not visible in its report.
result <- validate_gtfs("path-to-gtfs.zip", download_validator = TRUE)
result$notices
# Save the full HTML and JSON reports only when needed:
result <- validate_gtfs("path-to-gtfs.zip", download_validator = TRUE,
output_dir = "gtfs-validation-report")
result$html_path
An existing JAR can instead be supplied via validator_jar, or the
GTFS_VALIDATOR_JAR environment variable. Validation of an imported feed
checks its exported representation; it is not a certification of the original
archive. Java 17 or newer and jsonlite are required in either mode.
library(GTFSwizard)
gtfs <- GTFSwizard::read_gtfs('path-to-gtfs.zip') # or
gtfs <- GTFSwizard::as_wizardgtfs(gtfs_obj)
created_gtfs <- GTFSwizard::create_gtfs(
agency = data.frame(
agency_id = "A", agency_name = "Demo Transit",
agency_url = "https://example.com",
agency_timezone = "America/Fortaleza"
),
routes = data.frame(
route_id = "R1", agency_id = "A", route_short_name = "1",
route_long_name = "Central", route_type = 3
),
trips = data.frame(route_id = "R1", service_id = "WK", trip_id = "T1"),
stop_times = data.frame(
trip_id = "T1", arrival_time = c("08:00:00", "08:10:00"),
departure_time = c("08:00:00", "08:10:00"),
stop_id = c("S1", "S2"), stop_sequence = 1:2
),
stops = data.frame(
stop_id = c("S1", "S2"), stop_name = c("First", "Second"),
stop_lat = c(-3.73, -3.74), stop_lon = c(-38.52, -38.53)
),
calendar = data.frame(
service_id = "WK", monday = 1, tuesday = 1, wednesday = 1,
thursday = 1, friday = 1, saturday = 0, sunday = 0,
start_date = "20260101", end_date = "20261231"
)
)
summary(for_bus_gtfs)
# <summary.wizardgtfs>
# Agency: ETUFOR
# Service: 2019-09-13 to 2021-12-13 (823 active dates)
# 345 routes; 85410 trips; 4676 stops; 675 shapes
# Median consecutive-stop spacing: 268.2 m
#
# Tables:
# agency calendar calendar_dates fare_attributes fare_rules
# 1 3 6 2 345
# routes shapes stop_times stops trips
# 345 125776 2659737 4676 85410
explore_gtfs() opens an interactive dashboard with maps and planning and operational views. Omit the argument to choose a GTFS .zip file in a browse window.
GTFSwizard::explore_gtfs(for_bus_gtfs)
GTFSwizard::explore_gtfs()
The concept of a service_pattern in GTFSwizard helps address a common limitation of GTFS: it does not identify recurring combinations of services active on a date. A pattern is the exact set of service_id values operating on a calendar date, independent of route.
In wizardgtfs objects, the dates_services table is an extended feature that consolidates dates and associated service_ids into a single, organized table. This table is not standard in typical GTFS files but is added specifically in wizardgtfs objects. The dates_services table is structured so that each date is associated with a list of service_ids representing the transit services operating on that specific day. Essentially, each unique list of service_ids observed across dates defines a distinct service pattern. It is common to observe at least 3 service patterns: weekdays, saturdays and sundays. Dates inside the feed calendar range with no active services are treated as an explicit empty service set: trip counts are 0, and the calendar-level pattern is labeled "No service".
dates_services: Each date in the dates_services table has an associated list of service_ids, capturing the set of services active on that particular day.service_pattern is identified by a unique combination of service_ids operating on a given date. For instance, if two dates share the exact same service_ids, they are considered part of the same service_pattern.service_ids is represented as the "No service" pattern in calendar-level outputs and by get_servicepattern() with service_id = NA. It is not a trip-bearing pattern and cannot be used with filter_servicepattern().You can check service_pattern using the get_servicepattern() function.
GTFSwizard::get_servicepattern(for_bus_gtfs)
## A tibble: 3 × 3
# service_id service_pattern pattern_frequency
# <chr> <chr> <int>
#1 U servicepattern-1 586
#2 D servicepattern-2 121
#3 S servicepattern-3 116
Most analytical functions account for service patterns, including get_frequency() and plot_routefrequency(). Patterns are ordered from most frequent (a useful typical-day default) to less frequent, and route-frequency plots compare the represented patterns.
GTFSwizard::get_frequency(for_bus_gtfs, method = "by_route")
## A tibble: 1,763 × 5
# route_id direction_id service_pattern pattern_frequency daily.frequency
# <chr> <dbl> <chr> <int> <int>
#1 004 0 servicepattern-1 586 22
#2 004 1 servicepattern-1 586 23
#3 011 1 servicepattern-1 586 95
#4 011 1 servicepattern-2 121 43
#5 011 1 servicepattern-3 116 73
#6 012 0 servicepattern-1 586 102
## ℹ 1,757 more rows
## ℹ Use `print(n = ...)` to see more rows
GTFSwizard::plot_routefrequency(for_bus_gtfs, route = for_bus_gtfs$routes$route_id[3])
You can use plot_calendar() to check the number of trips along the calendar and get a better sense of the service_pattern rationale. Dates without active service appear as 0 trips in trip-count mode and as "No service" in service-pattern mode.
GTFSwizard::plot_calendar(for_bus_gtfs, facet_by_year = TRUE)
Frequency, headways, dwell times, speeds, durations, distances, fleet requirements, first departures, corridors, and hubs can be calculated directly from the schedule. Several functions support aggregation methods such as by_trip, by_route, by_hour, and detailed; see each function's help page for its exact observational unit.
GTFSwizard::get_headways(for_bus_gtfs, method = 'by_hour')
## A tibble: 73 × 5
# hour service_pattern pattern_frequency headway_minutes valid_trips
# <dbl> <chr> <int> <dbl> <int>
#1 0 servicepattern-1 586 11.6 8
#2 0 servicepattern-2 121 11.6 8
#3 0 servicepattern-3 116 11.6 8
#4 1 servicepattern-1 586 64.4 32
#5 1 servicepattern-2 121 64.4 32
#6 1 servicepattern-3 116 64.4 32
## ℹ 67 more rows
## ℹ Use `print(n = ...)` to see more rows
GTFSwizard::get_durations(for_bus_gtfs, method = 'detailed', trips = 'all')
GTFSwizard::get_distances(for_bus_gtfs, method = 'by_trip', trips = 'all')
GTFSwizard::get_distances(for_bus_gtfs, method = 'by_route', trips = 'all')
GTFSwizard::get_speeds(for_bus_gtfs, method = 'by_route', trips = 'all')
GTFSwizard::get_fleet(for_bus_gtfs, method = 'peak')
GTFSwizard::get_1stdeparture(for_bus_gtfs)
Corridors and hubs are simplified representations of critical links and nodes on transit networks.
get_corridor() retrieves and plot_corridor() visualizes high-density transit sections.GTFSwizard::get_corridor(for_bus_gtfs, i = .01, min_length = 1500)
# Simple feature collection with 4 features and 4 fields
# Geometry type: MULTILINESTRING
# Dimension: XY
# Geodetic CRS: WGS 84
# # A tibble: 4 × 5
# corridor stop_id trip_id length geometry
# <chr> <list> <list> <dbl> <MULTILINESTRING [°]>
# 1 Corridor 1 <chr [7]> <chr [3,429]> 2851. ((-38.48122 -3.781901, ...)
# 2 Corridor 2 <chr [5]> <chr [2,504]> 2214. ((-38.54677 -3.731971, ...)
# 3 Corridor 3 <chr [5]> <chr [3,470]> 2089. ((-38.48102 -3.782528, ...)
# 4 Corridor 4 <chr [7]> <chr [3,104]> 1635. ((-38.55838 -3.780695, ...)
GTFSwizard::plot_corridor(for_bus_gtfs)
get_hubs() retrieves and plot_hubs() visualizes high-connectivity transit stops.GTFSwizard::get_hubs(for_bus_gtfs)
# Simple feature collection with 4676 features and 5 fields
# Geometry type: POINT
# # A tibble: 4,676 × 6
# stop_id trip_id route_id n_trip n_routes geometry
# <chr> <list> <list> <int> <int> <POINT [°]>
# 1 6079 <chr [14,745]> <chr [65]> 14745 65 (-38.48476 -3.738568)
# 2 4030 <chr [6,405]> <chr [62]> 6405 62 (-38.50203 -3.830385)
# 3 6083 <chr [15,578]> <chr [56]> 15578 56 (-38.56358 -3.775878)
# 4 5822 <chr [14,364]> <chr [52]> 14364 52 (-38.58683 -3.789329)
# 5 1717 <chr [4,252]> <chr [43]> 4252 43 (-38.5345 -3.735906)
# 6 6449 <chr [4,252]> <chr [43]> 4252 43 (-38.53651 -3.738107)
## ℹ Use `print(n = ...)` to see more rows
GTFSwizard::plot_hubs(for_bus_gtfs)
Filtering tools allow GTFS data to be restricted by service patterns, dates, service IDs, route IDs, trip IDs, stop IDs, and time ranges. These filter_ functions retain the relevant records and their GTFS relationships.
filter_servicepattern(): Filter by specified active service patterns. Defaults to the most frequent active pattern (typical day) if none is provided. The "No service" pattern describes calendar days without trips and is not accepted by this filter.filter_date(): Filter data by specific dates, returning only services active on those dates.filter_service(): Filter by specific service IDs to retain.filter_route(): Filter by route ID. Set keep = TRUE to retain specified routes or keep = FALSE to exclude them.filter_trip(): Filter by trip ID. Set keep = TRUE to retain specified trips or keep = FALSE to exclude them.filter_stop(): Keep stop calls at the requested stop IDs. Trips may remain partial, which supports route and network experiments.filter_time(): Keep stop calls inside a specified time range (from and to). Trips that cross the time boundary remain as partial trips.# Filter by service pattern
filtered_gtfs <- GTFSwizard::filter_servicepattern(for_bus_gtfs, "servicepattern-2")
# Filter by specific date
filtered_gtfs <- GTFSwizard::filter_date(for_bus_gtfs, "2021-02-10")
# Filter by route ID, retaining only specified routes
filtered_gtfs <- GTFSwizard::filter_route(for_bus_gtfs, for_bus_gtfs$routes$route_id[1:2])
# Filter by trip ID, excluding specified trips
filtered_gtfs <- GTFSwizard::filter_trip(for_bus_gtfs, for_bus_gtfs$trips$trip_id[1:2], FALSE)
# Filter by time range
filtered_gtfs <- GTFSwizard::filter_time(gtfs = for_bus_gtfs, "06:30:00", "10:00:00")
# Spatial filter using filter_stop
spatial.filter <- GTFSwizard::get_shapes_sf(for_bus_gtfs$shapes)
stops <- sf::st_filter(GTFSwizard::get_stops_sf(for_bus_gtfs$stops),
spatial.filter) |>
dplyr::pull(stop_id)
filtered_gtfs <- GTFSwizard::filter_stop(for_bus_gtfs, stops)
selection() records a subset or grouping without removing any GTFS rows. Bare columns create groups in a style similar to dplyr::group_by(), while logical expressions restrict the records represented by the selection metadata.
# One group for each route and direction
grouped_gtfs <- GTFSwizard::selection(
for_bus_gtfs,
route_id,
direction_id
)
# Group selected routes without modifying the original GTFS tables
selected_gtfs <- GTFSwizard::selection(
for_bus_gtfs,
route_id,
route_id %in% for_bus_gtfs$routes$route_id[1:3]
)
attr(selected_gtfs, "selection")$groups
selected_gtfs <- GTFSwizard::unselection(selected_gtfs)
GTFSwizard provides consistent static plots for network supply and scheduled operations. Plot subtitles and axes state the observational unit represented.
plot_frequency() shows the distribution of trip frequencies by hour, with hourly and overall averages to highlight peak service times.GTFSwizard::plot_frequency(for_bus_gtfs)
plot_routefrequency() displays a tile plot where fill is the number of scheduled trips by route, hour, and service pattern. Use top_n to keep large feeds readable.GTFSwizard::plot_routefrequency(for_bus_gtfs, route = for_bus_gtfs$routes$route_id[4:5])
plot_headways() shows average time between trips, highlighting hourly and overall headways to visualize service intervals.GTFSwizard::plot_headways(for_bus_gtfs)
plot_servicespan() shows the first departure and final arrival for each route-service pattern, plot_serviceheatmap() summarizes departures by weekday and hour, plot_routeduration() shows scheduled trip-duration distributions, and plot_servicesupply() reports scheduled vehicle-hours.GTFSwizard::plot_servicespan(for_bus_gtfs)
GTFSwizard::plot_serviceheatmap(for_bus_gtfs)
GTFSwizard::plot_routeduration(for_bus_gtfs)
GTFSwizard::plot_servicesupply(for_bus_gtfs)
GTFSwizard provides functions to edit GTFS data directly.
The delay_trip() function allows users to apply a delay to specific trips.
The split_trip() function divides a trip into valid consecutive parts. Trips can be split into split + 1 approximately equal parts, or at selected internal stop IDs using the stops argument. Each boundary stop is retained in both adjacent parts; supply either split or stops, not both. This can be useful for analyzing partial routes or for simulating route adjustments.
The edit_speed() function adjusts the travel speeds between stops in a GTFS dataset by modifying trip durations based on a specified speed multiplier. It allows selective adjustments for specific trips and stops or applies changes globally.
The set_dwelltime() function overwrites selected dwell times while retaining arrival at each edited stop and shifting downstream times. The trip end time may therefore change. The edit_dwelltime() function scales selected dwell times and applies the same downstream propagation.
The merge_gtfs() function combines two GTFS files, allowing for the integration of distinct GTFS datasets into a single dataset.
# Delay trips by 5 minutes (300 seconds)
delayed_gtfs <- GTFSwizard::delay_trip(for_bus_gtfs, trip = for_bus_gtfs$trips$trip_id[1:2], duration = 300)
# Split a trip in 3 sections (2 splits)
split_gtfs <- GTFSwizard::split_trip(for_bus_gtfs, trip = for_bus_gtfs$trips$trip_id[1:2], split = 2)
# Split a trip at selected stop IDs
trip_id <- for_bus_gtfs$trips$trip_id[1]
trip_times <- for_bus_gtfs$stop_times[for_bus_gtfs$stop_times$trip_id == trip_id, ]
trip_times <- trip_times[order(trip_times$stop_sequence), ]
split_at_stop_gtfs <- GTFSwizard::split_trip(for_bus_gtfs, trip = trip_id, stops = trip_times$stop_id[2])
# Merge two GTFS files into one
merged_gtfs <- GTFSwizard::merge_gtfs(for_bus_gtfs, for_rail_gtfs)
# Double the speed of all trips
faster_gtfs <- GTFSwizard::edit_speed(for_rail_gtfs, factor = 2)
# Set and edit dwell times for specific trips
gtfs <- set_dwelltime(for_rail_gtfs,
trips = for_rail_gtfs$trips$trip_id[1:100],
stops = for_rail_gtfs$stops$stop_id[1:20],
duration = 10)
gtfs <- edit_dwelltime(gtfs,
trips = for_rail_gtfs$trips$trip_id[1:100],
stops = for_rail_gtfs$stops$stop_id[1:20],
factor = 1.5)
get_dwelltimes(gtfs, method = 'detailed')
Export a resulting feed with write_gtfs(), which writes a standard GTFS .zip archive to the requested path.
GTFSwizard::write_gtfs(for_bus_gtfs, 'path-to-file.zip')
GTFSwizard implements the tidytransit::raptor() algorithm that estimates travel time matrices from a wizardgtfs object and a few other arguments.
GTFSwizard::tidy_raptor(for_rail_gtfs, min_departure = '06:20:00', max_arrival = '09:40:00',
dates = "2021-12-13", max_transfers = 2, keep = "all",
stop_ids = '66')
GTFSwizard autodetects and reconstructs missing shape tables using the get_shapes() function. Variations of this function can create simple feature objects from stops or shapes tables, using get_stops_sf() or get_shapes_sf() functions, or standard GTFS shapes data frames from simple-feature shape objects using get_shapes_df(). Because get_shapes() reconstructs geometry from stop sequences, shapes created after filter_stop() or filter_time() describe only the retained partial trips.
gtfs <- for_bus_gtfs
gtfs$shapes <- NULL
gtfs$shapes
#NULL
gtfs <- GTFSwizard::get_shapes(gtfs)
GTFSwizard::get_shapes_sf(for_bus_gtfs$shapes)
GTFSwizard::get_stops_sf(for_bus_gtfs$stops)
The latlon2epsg() function determines the appropriate UTM (Universal Transverse Mercator) EPSG code for a given sf object based on its centroid's latitude and longitude. This can be very useful for conveniently geoprocessing data in meters.
GTFSwizard::latlon2epsg(get_shapes_sf(for_bus_gtfs)$shapes)
GTFSwizard features two toy examples, a small for_rail_gtfs wizardgtfs object, and a rather bigger for_bus_gtfs wizardgtfs object. They are real GTFS samples, the first being the urban subway system, and the second one the regular bus system; both for the city of Fortaleza, Brazil, on the 2020's.
gtfs <- GTFSwizard::for_bus_gtfs
plot(gtfs)
The functions described facilitate the analysis, simulation, and evaluation of public transportation systems. They assist the replication of real-world transit interventions, enabling researchers, planners, and policymakers to test and refine system modifications in a controlled and efficient manner. Key applications are outlined below along with their potential uses in addressing typical challenges and opportunities in public transit systems.
Bus Rapid Transit (BRT) and Exclusive Lanes: BRT systems often rely on exclusive
corridors to reduce travel times and enhance reliability. Using edit_speed(), users can
represent changes towards smaller travel times on these corridors by adjusting travel
speeds. In addition, edit_dwelltime() allows the representation of optimized boarding
and alighting processes, reflecting reduced dwell times at stations due to level boarding,
pre-payment mechanisms, or increased operational efficiency;
Frequency Modifications: Frequency adjustments are among the most common transit
interventions. By using filter_trip() to select trips to be doubled, delay_trip() to
change its first departure_time, and merge_gtfs() to add them to the original GTFS,
users can represent increased frequencies, reflecting higher service levels. Conversely,
filter_trip can be used to reduce service frequencies, allowing for the evaluation of
cost-saving measures or temporary schedule adjustments;
Route Segmentation and Partial Adjustments: With split_trip(), users can divide
existing routes into smaller segments, enabling partial route adjustments. This is
particularly relevant in studies assessing the feasibility of feeder services, route
rationalization, or service redundancy elimination.
Express Services: Transit stops significantly influence travel times, operating costs, and
network coverage. Using edit_dwelltime() and filter_stop() can subtract dwell times from
total trip durations and remove unused stops, representing the introduction of express
services.
See the GTFSwizard cheat sheet for a compact guide to the main workflow: creating or reading feeds, selecting services, plotting operations, editing GTFS, exploring feeds interactively, and writing results.
Contributions are welcome! To report a bug, suggest a feature, or contribute code, please use the repository’s Issues.
GTFSwizard mainly uses dplyr for data handling, sf for spatial operations, and ggplot2 for static visualization. shiny and leaflet are optional dependencies for explore_gtfs(). tidytransit, data.table, and hms are optional dependencies used only by tidy_raptor(). jsonlite supports validate_gtfs(); openssl verifies its optional validator download.
The following citation refers to the CRAN release. Use citation("GTFSwizard")
to obtain the citation for the installed version, including a development version.
A BibTeX entry for LaTeX users is
@Manual{quesado.guimaraes.oliveiraneto.2026,
title = {GTFSwizard: Creating, Exploring and Manipulating GTFS Files},
author = {N. O. {Quesado Filho} and C. G. C. {Guimaraes} and F. M. {de Oliveira Neto}},
year = {2026},
note = {R package version 1.2.1},
url = {https://cran.r-project.org/package=GTFSwizard},
doi = {10.32614/CRAN.package.GTFSwizard}}
QUESADO FILHO, N. O.; GUIMARAES, C. G. C. ; OLIVEIRA NETO, F. M. . How to use GTFSwizard R Package to Assess Transit Projects: Fortaleza's BR-116 Bus Rapid Transit System Proposal. In: 39º Congresso de Pesquisa e Ensino em Transportes, 2025, Goiânia. Anais do 39º Congresso de Pesquisa e Ensino em Transportes, 2025.
GTFSwizard is developed by Nelson Quesado, Caio Guimarães and Fco. Moraes at OPA-TP research group, Universidade Federal do Ceará.