Package {r5r}

Type:	Package
Title:	Rapid Realistic Routing with 'R5'
Version:	2.5.0
Description:	Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 'R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine https://github.com/conveyal/r5. The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at https://docs.conveyal.com/. Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see https://docs.conveyal.com/changelog) corresponds with the R5 version that r5r depends on.
License:	GPL (≥ 3) | MIT + file LICENSE
URL:	https://github.com/ipea/r5r, https://ipea.github.io/r5r/
BugReports:	https://github.com/ipea/r5r/issues
Depends:	R (≥ 4.1.0)
Imports:	checkmate, cli, data.table, isoband, jsonlite, lifecycle, methods, purrr, rJava (≥ 0.9-10), rlang, sf (≥ 1.0-12), sfheaders, utils, zip
Suggests:	accessibility, covr, dplyr, fs, ggplot2 (≥ 3.3.1), gtfstools (≥ 2.0.0), h3jsr (≥ 1.3.0), interp, knitr, mapview, parallel, patchwork, rJavaEnv, rmarkdown, testthat, withr
VignetteBuilder:	knitr
Encoding:	UTF-8
SystemRequirements:	Java JDK (>= 21.0)
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-11-05 02:16:40 UTC; r1701707
Author:	Marcus Saraiva [aut], Rafael H. M. Pereira [aut, cre], Daniel Herszenhut [aut], Alex Magnus [aut], Matthew Wigginton Bhagat-Conway [aut], Carlos Kaue Vieira Braga [ctb], Luyu Liu [ctb], Egor Kotov [ctb], Daniel Snow [ctb], Ipea - Institute for Applied Economic Research [cph, fnd], Department of Geography & Planning, University of Toronto [fnd]
Maintainer:	Rafael H. M. Pereira <rafa.pereira.br@gmail.com>
Repository:	CRAN
Date/Publication:	2026-11-07 23:50:02 UTC

r5r: Rapid Realistic Routing with 'R5'

Description

Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using R5, the Rapid Realistic Routing on Real-world and Reimagined networks engine https://github.com/conveyal/r5. The package allows users to generate detailed routing analysis or calculate travel time matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at https://docs.conveyal.com/. Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm if the R5 version implied by the Conveyal user manual (see https://docs.conveyal.com/changelog) corresponds with the R5 version that r5r depends on.

Usage

See the vignettes on the website.

Author(s)

Maintainer: Rafael H. M. Pereira rafa.pereira.br@gmail.com (ORCID)

Authors:

• Rafael H. M. Pereira rafa.pereira.br@gmail.com (ORCID)

• Marcus Saraiva marcus.saraiva@gmail.com (ORCID)

• Daniel Herszenhut dhersz@gmail.com (ORCID)

• Alex Magnus alex.magnus@mail.utoronto.ca

• Matthew Wigginton Bhagat-Conway mwbc@unc.edu (ORCID)

Other contributors:

• Carlos Kaue Vieira Braga kaue@kauebraga.dev (ORCID) [contributor]

• Luyu Liu luyuliu@ufl.edu (ORCID) [contributor]

• Egor Kotov kotov.egor@gmail.com (ORCID) [contributor]

• Daniel Snow dan@sno.ws [contributor]

• Ipea - Institute for Applied Economic Research [copyright holder, funder]

• Department of Geography & Planning, University of Toronto [funder]

See Also

Useful links:

• https://github.com/ipea/r5r

• https://ipea.github.io/r5r/

• Report bugs at https://github.com/ipea/r5r/issues

Calculate access to opportunities

Description

Fast computation of access to opportunities given a selected decay function.

Usage

accessibility(
  r5r_network,
  origins,
  destinations,
  opportunities_colnames = "opportunities",
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  percentiles = 50L,
  decay_function = "step",
  cutoffs = NULL,
  decay_value = NULL,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  fare_structure = NULL,
  max_fare = Inf,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  seed = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A data.table with accessibility estimates for all origin points. This data.table contains the columns id (origin id), opportunity (the type of opportunities to which accessibility was calculated), percentile (the travel time percentile considered in the estimate), cutoff (the specified cutoff values, except when decay_function is fixed_exponential, in which case the cutoff parameter is not used) and accessibility (the accessibility estimate). Origins that cannot be snapped to the street network get an accessibility of 0 (see find_snap()). If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved. With fixed_exponential, these files keep a cutoff column filled with the placeholder value 0.

Decay functions

R5 allows one to use different decay functions when calculating accessibility. See the original R5 documentation from Conveyal for more information on each one (https://docs.conveyal.com/learn-more/decay-functions). A summary of each available option, as well as the value passed to decay_function to use it (inside parentheses) are listed below:

• Step, also known as cumulative opportunities ("step"):
  a binary decay function used to find the sum of available opportunities within a specific travel time cutoff.

• Logistic CDF ("logistic"):
  This is the logistic function, i.e. the cumulative distribution function of the logistic distribution, expressed such that its parameters are the median (inflection point) and standard deviation. This function applies a sigmoid rolloff that has a convenient relationship to discrete choice theory. Its parameters can be set to reflect a whole population's tolerance for making trips with different travel times. The function's value represents the probability that a randomly chosen member of the population would accept making a trip, given its duration. Opportunities are then weighted by how likely it is that a person would consider them "reachable".

  • Calibration: The median parameter is controlled by the cutoff parameter, leaving only the standard deviation to configure through the decay_value parameter.

• Fixed Exponential ("fixed_exponential"):
  This function is of the form exp(-Lt) where L is a single fixed decay constant in the range (0, 1) and t is the travel time in seconds. It is constrained to be positive to ensure weights decrease (rather than grow) with increasing travel time. Note that L is a per-second constant: to use a decay constant expressed per minute (e.g. from the literature), pass it divided by 60.

  • Calibration: This function is controlled exclusively by the L constant, given by the decay_value parameter. cutoffs must be NULL.

• Half-life Exponential Decay ("exponential"):
  This is similar to the fixed-exponential option above, but in this case the decay parameter is inferred from the cutoffs parameter values, which is treated as the half-life of the decay.

• Linear ("linear"):
  This is a simple, vaguely sigmoid option, which may be useful when you have a sense of a maximum travel time that would be tolerated by any traveler, and a minimum time below which all travel is perceived to be equally easy.

  • Calibration: The transition region is transposable and symmetric around the cutoffs parameter values, taking decay_value minutes to taper down from one to zero. decay_value must be a whole number of minutes between 1 and 59.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

Examples

library(r5r)

data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)
points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:500, ]

departure_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

access <- accessibility(
  r5r_network ,
  origins = points,
  destinations = points,
  opportunities_colnames = "schools",
  mode = "WALK",
  departure_datetime = departure_datetime,
  decay_function = "step",
  cutoffs = 30,
  max_trip_duration = 30
)
head(access)

# using a different decay function
access <- accessibility(
  r5r_network ,
  origins = points,
  destinations = points,
  opportunities_colnames = "schools",
  mode = "WALK",
  departure_datetime = departure_datetime,
  decay_function = "logistic",
  cutoffs = 30,
  decay_value = 1,
  max_trip_duration = 30
)
head(access)

# using several cutoff values
access <- accessibility(
  r5r_network ,
  origins = points,
  destinations = points,
  opportunities_colnames = "schools",
  mode = "WALK",
  departure_datetime = departure_datetime,
  decay_function = "step",
  cutoffs = c(15, 30),
  max_trip_duration = 30
)
head(access)

# calculating access to different types of opportunities
access <- accessibility(
  r5r_network ,
  origins = points,
  destinations = points,
  opportunities_colnames = c("schools", "healthcare"),
  mode = "WALK",
  departure_datetime = departure_datetime,
  decay_function = "step",
  cutoffs = 30,
  max_trip_duration = 30
)
head(access)

stop_r5(r5r_network )

Calculate travel time matrix between origin destination pairs considering a time of arrival

Description

Computes travel times between origin-destination pairs for a given arrival time: for each pair, the trip with the latest departure that arrives by arrival_datetime. Departures are searched minute by minute between arrival_datetime - max_trip_duration and arrival_datetime, so max_trip_duration also sets the search window. For a departure time, use travel_time_matrix(). This function wraps expanded_travel_time_matrix(), so its output has more columns than that of travel_time_matrix(), and it can be very memory intensive with a long max_trip_duration. destinations can have at most 5000 rows, a limit of R5 for detailed path information; split larger sets into chunks.

Usage

arrival_travel_time_matrix(
  r5r_network,
  origins,
  destinations,
  mode = "WALK",
  mode_egress = "WALK",
  arrival_datetime,
  breakdown = FALSE,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  seed = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A data.table with one row per origin-destination pair that can be reached by arrival_datetime, describing the trip with the latest departure that still arrives in time: its departure_time, the routes used and its total_time (in minutes), plus the columns added by breakdown = TRUE (see expanded_travel_time_matrix()). Pairs that cannot be reached in time are absent from the output. When the search window crosses midnight, departure times after midnight are reported as "24:MM:SS", and only the public transport services of the departure day are considered. Trips made only by walking, cycling or driving have travel times in whole minutes, so they can arrive up to 59 seconds after arrival_datetime. If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

See Also

Other routing: detailed_itineraries(), expanded_travel_time_matrix(), pareto_frontier(), travel_time_matrix()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path )

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))

arrival_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

# by default only returns the total time between each pair in each minute of
# the specified time window
arrival_ttm <- arrival_travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  arrival_datetime = arrival_datetime,
  max_trip_duration = 60
)

head(arrival_ttm)

# when breakdown = TRUE the output contains much more information
arrival_ttm2 <- arrival_travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  arrival_datetime = arrival_datetime,
  max_trip_duration = 60,
  breakdown = TRUE
)

head(arrival_ttm2)

stop_r5(r5r_network)

Assert fare structure

Description

Asserts whether the specified fare structure object complies with the structure set in setup_fare_structure().

Usage

assert_fare_structure(fare_structure)

Arguments

Value

Throws and error upon failure and invisibly returns TRUE on success.

Assign decay function and parameter values

Description

Checks and assigns decay function and values.

Usage

assign_decay_function(decay_function, decay_value)

Arguments

Value

A list with the validated decay function and parameter value.

See Also

Other assigning functions: assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Check and convert POSIXct objects to strings

Description

Check and convert POSIXct objects to strings

Usage

assign_departure(datetime, mode_list)

Arguments

Value

A list with the date and time of the trip departure as characters.

See Also

Other assigning functions: assign_decay_function(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Assign drop geometry

Description

Check the drop geometry input.

Usage

assign_drop_geometry(drop_geometry)

Arguments

Value

A logical.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Assign max street time from walk/bike distance and speed

Description

Checks the time duration and speed inputs and converts them to distance.

Usage

assign_max_street_time(max_time, speed, max_trip_duration, mode)

Arguments

Value

An integer representing the maximum number of minutes walking.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Assign max trip duration

Description

Check and convert the max trip duration input.

Usage

assign_max_trip_duration(
  max_trip_duration,
  modes,
  max_walk_time,
  max_bike_time,
  max_car_time
)

Arguments

Value

An integer representing the maximum trip duration in minutes.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Check and select transport modes from user input

Description

Selects the transport modes used in the routing functions. Only one direct and access/egress modes are allowed at a time.

Usage

assign_mode(mode, mode_egress)

Arguments

Value

A list with the transport modes to be used in the routing.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_opportunities(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Assign opportunities data

Description

Check and create an opportunities dataset.

Usage

assign_opportunities(destinations, opportunities_colnames)

Arguments

Value

A list of Java-Array objects.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_osm_link_ids(), assign_points_input(), assign_shortest_path()

Assign osm_link_ids

Description

Check the osm_link_ids input.

Usage

assign_osm_link_ids(osm_link_ids, drop_geometry)

Arguments

Value

A logical.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_points_input(), assign_shortest_path()

Check and convert origin and destination inputs

Description

Check and convert origin and destination inputs

Usage

assign_points_input(df, name, unique_ids = TRUE)

Arguments

Value

A data.frame with columns id, lon and lat.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_shortest_path()

Assign shortest path

Description

Check the shortest path input.

Usage

assign_shortest_path(shortest_path)

Arguments

Value

A logical.

See Also

Other assigning functions: assign_decay_function(), assign_departure(), assign_drop_geometry(), assign_max_street_time(), assign_max_trip_duration(), assign_mode(), assign_opportunities(), assign_osm_link_ids(), assign_points_input()

Build a transport network used for routing in R5

Description

Builds a multimodal transport network used for routing in R5, combining multiple data inputs present in the directory where the network should be saved to. The directory must contain only one street network file (in .osm.pbf format). It may optionally contain one or more public transport GTFS feeds (in .zip format), when used for public transport routing, and a .tif file describing the elevation profile of the study area. If there is more than one GTFS feed in the directory, all feeds are automatically merged. If there is already a 'network.dat' file in the directory, the function reads it instead (see overwrite).

Usage

build_network(
  data_path,
  verbose = FALSE,
  temp_dir = FALSE,
  elevation = "TOBLER",
  overwrite = FALSE
)

Arguments

Value

An r5r_network object, used by r5r's routing and support functions.

Elevation

More information about the TOBLER and MINETTI options to calculate the effects of elevation on travel times can be found in the references below:

• Campbell, M. J., et al (2019). Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied geography, 106, 93-107. doi:10.1016/j.apgeog.2019.03.008.

• Minetti, A. E., et al (2002). Energy cost of walking and running at extreme uphill and downhill slopes. Journal of applied physiology. doi:10.1152/japplphysiol.01177.2001.

• Tobler, W. (1993). Three presentations on geographical analysis and modeling: Non-isotropic geographic modeling speculations on the geometry of geography global spatial analysis. Technical Report. National center for geographic information and analysis. 93 (1). https://escholarship.org/uc/item/05r820mz.

See Also

Other Build network: download_r5(), setup_r5()

Examples

library(r5r)

# directory with street network and gtfs files
data_path <- system.file("extdata/poa", package = "r5r")

r5r_network <- build_network(data_path)

Check transit service availability by date

Description

Counts the public transport services in the GTFS feeds of a r5r_network that are active on given dates, and their proportion of all services. Use it to pick a departure date with adequate service: public transport routing only finds trips on dates when services run (per the GTFS calendars and date exceptions).

Usage

check_transit_availability(
  r5r_network,
  dates = NULL,
  start_date = NULL,
  end_date = NULL,
  r5r_core = deprecated()
)

Arguments

Details

When no date arguments are supplied, the function discovers all dates with an active service referenced by a retained transit trip, including frequency-based trips. This accounts for weekday calendars and date exceptions, including exception-only services. A date with active service does not guarantee a trip at every departure time or between every pair of points.

You can also specify the dates to check in two ways:

• Using the dates argument to provide a vector of specific dates.

• Using the start_date and end_date arguments to provide a continuous date range.

You cannot combine these methods. In all modes, total_services counts every imported service, not only those used by trips; counts are of services, not trips. If calendar bounds are invalid, automatic discovery reports the affected service; explicit date queries remain available.

Value

A data.table with four columns: date, total_services, active_services, and pct_active (the proportion of active services). Automatic discovery returns dates in ascending order, or a zero-row table with these columns if no retained transit trips operate.

Examples

library(r5r)
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# Discover operating dates without knowing the feed's date range
availability1 <- check_transit_availability(r5r_network)
head(availability1)
# plot availability over the year
library(ggplot2)
ggplot(availability1, aes(x = date, y = pct_active)) +
  geom_line() +
  geom_point() +
  theme_classic(base_size = 16)

# Example 2: Check a vector of specific dates
# Let's check a regular weekday and a Sunday, where service may differ.
dates_to_check <- c("2019-05-13", "2019-05-19")
availability2 <- check_transit_availability(
  r5r_network,
  dates = dates_to_check
)
head(availability2)

# Example 3: Check a continuous date range using start_date and end_date
availability3 <- check_transit_availability(
  r5r_network,
  start_date = "2019-01-01",
  end_date = "2019-12-31"
)
availability3[121:124,]


stop_r5(r5r_network)

Save speeds polygon to .geojson temporary file

Description

Support function that checks the input of speeds polygon passed to build_custom_network() and saves it to a .geojson temporary file.

Usage

congestion_poly2geojson(new_speeds_poly)

Arguments

Value

The path to a .geojson saved as a temporary file.

See Also

Other Support functions: lts_lines2shp()

Examples

# read polygons with new speeds
congestion_poly <- readRDS(
  system.file("extdata/poa/poa_poly_congestion.rds", package = "r5r")
  )

geojson_path <- r5r:::congestion_poly2geojson(
  new_speeds_poly = congestion_poly
  )

Detailed itineraries between origin-destination pairs

Description

Returns detailed trip information between origin-destination pairs: the waiting and moving time, distance, route and geometry of each trip leg. Designed for trip planning (like Google's Directions API or OpenTripPlanner), it takes much more time and memory than the package's other, more analytical, routing functions.

Usage

detailed_itineraries(
  r5r_network,
  origins,
  destinations,
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  suboptimal_minutes = 0L,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  shortest_path = TRUE,
  all_to_all = FALSE,
  fare_structure = NULL,
  max_fare = Inf,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  drop_geometry = FALSE,
  osm_link_ids = FALSE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

When drop_geometry is FALSE, the function outputs a ⁠LINESTRING sf⁠ with detailed information on the itineraries between the specified origins and destinations. When TRUE, the output is a data.table. All distances are in meters and travel times are in minutes. Each row is one segment (trip leg) of one itinerary, with the columns:

• from_id, from_lat, from_lon, to_id, to_lat, to_lon: the origin and destination of the pair;

• option: the itinerary number within the pair. Options are ordered with direct (non-transit) trips first, then by number of segments, then by duration, so option 1 is not necessarily the fastest one. With shortest_path = TRUE, each pair has a single itinerary (option 1);

• departure_time: the departure time of the itinerary ("HH:MM:SS");

• total_duration, total_distance: duration and distance of the whole itinerary;

• segment, mode, segment_duration, wait, distance, route: the segment number, its transport mode, its in-motion duration, the waiting time before it, its distance and the transit route used (empty for non-transit segments);

• total_fare, cumulative_fare: only when a fare_structure is used;

• osm_id_list, edge_id_list, board_stop_id, alight_stop_id: only when osm_link_ids is TRUE;

• geometry: only when drop_geometry is FALSE.

If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved. This function writes one file per origin-destination pair, named ⁠from_<origin id>_to_<destination id>.csv⁠, so pairs must be unique and ids cannot contain characters that are not allowed in file names.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The pareto_frontier() function, and detailed_itineraries() with shortest_path = FALSE or a fare_structure, use an R5-specific extension to the McRAPTOR routing algorithm. The implementation used in detailed_itineraries() allows the router to find paths that are optimal and less than optimal in terms of travel time, with some heuristics around multiple access modes, riding the same patterns, etc. The specific extension to McRAPTOR to do suboptimal path routing is not documented yet, but a detailed description of base McRAPTOR can be found in Delling et al (2015). The implementation used in pareto_frontier(), on the other hand, returns only the fastest trip within a given monetary cutoff, ignoring slower trips that cost the same. A detailed discussion on the algorithm can be found in Conway and Stewart (2019).

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

• Conway, M. W., & Stewart, A. F. (2019). Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics. International Journal of Geographical Information Science, 33(9), 1759-1787. doi:10.1080/13658816.2019.1605075

Fastest itinerary

When only the fastest itinerary is returned (shortest_path = TRUE) and no fare_structure is used, public transport is routed with R5's RAPTOR algorithm (Conway et al, 2017), the one used by travel_time_matrix(), which is much faster than McRAPTOR. Compared with McRAPTOR, the results can differ slightly: departures are routed every minute of time_window instead of at random seconds, so the itinerary returned is never more than about a minute slower and often faster; walking transfers between stops are limited by max_walk_time (McRAPTOR only applies it to the first and last legs); stops where the GTFS pickup_type or drop_off_type does not allow boarding or alighting are respected; and when two trips are equally fast, the route chosen may differ.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

See Also

Other routing: arrival_travel_time_matrix(), expanded_travel_time_matrix(), pareto_frontier(), travel_time_matrix()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))

# inputs
departure_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

det <- detailed_itineraries(
  r5r_network,
  origins = points[10,],
  destinations = points[12,],
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  max_trip_duration = 60
)
head(det)

stop_r5(r5r_network)

Download R5.jar

Description

Downloads R5.jar and saves it locally, inside the package directory.

Usage

download_r5(
  version = NULL,
  quiet = FALSE,
  force_update = FALSE,
  temp_dir = FALSE
)

Arguments

Value

The path to the downloaded file.

See Also

Other Build network: build_network(), setup_r5()

Examples

library(r5r)

download_r5(temp_dir = TRUE)

data.table to ltsMap

Description

Converts a data.frame with road OSM id's and respective LTS levels a Java Map<Long, Integer> for use by r5r_network.

Usage

dt_to_lts_map(dt)

Arguments

Value

A speedMap (Java HashMap<Long, Integer>)

See Also

Other java support functions: dt_to_speed_map(), get_java_version(), java_to_dt(), run_routing_job()

data.table to speedMap

Description

Converts a data.frame with road OSM id's and respective speeds to a Java Map<Long, Float> for use by r5r_network.

Usage

dt_to_speed_map(dt)

Arguments

Value

A speedMap (Java HashMap<Long, Float>)

See Also

Other java support functions: dt_to_lts_map(), get_java_version(), java_to_dt(), run_routing_job()

Check if there is an elevation data file in .tif format in a given data path

Description

Check if there is an elevation data file in .tif format in a given data path

Usage

exists_tiff(data_path)

Arguments

Value

TRUE if there is a .tif file, FALSE otherwise

See Also

Other support functions: fileurl_from_metadata(), get_gtfs_errors(), start_r5r_java(), stop_r5(), tempdir_unique(), travel_time_surface(), validate_bad_osm_ids()

Expand origin-destination pairs

Description

This function is used in detailed_itineraries() to update the origins and destinations datasets.

Usage

expand_od_pairs(origins, destinations, all_to_all)

Arguments

Calculate minute-by-minute travel times between origin destination pairs

Description

Detailed computation of travel time estimates between one or multiple origin destination pairs. Results show the travel time of the fastest route alternative departing each minute within a specified time window. This function can be very memory intensive for large data sets and time windows. destinations can have at most 5000 rows, a limit of R5 for detailed path information; split larger sets into chunks.

Usage

expanded_travel_time_matrix(
  r5r_network,
  origins,
  destinations,
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  breakdown = FALSE,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  seed = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A data.table with travel time estimates (in minutes) and the routes used in each trip between origin and destination pairs, for each minute of the specified time window. Each set of origin, destination and departure minute can appear up to N times, where N is the number of Monte Carlo draws set by draws_per_minute (this only applies when the GTFS feeds include a frequencies table; otherwise a single draw is performed). A pair is completely absent from the final output if no trips could be completed in any of the minutes of the time window. If for a single pair trips could be completed in some of the minutes of the time window, but not for all of them, the minutes in which trips couldn't be completed will have NA travel time and routes used. A pair may also appear with NA in every minute, when its only trips are slightly longer than max_trip_duration (e.g. walk-only trips, whose time to walk between the point and the street network is added after the limit is applied).

The output has the columns from_id, to_id, departure_time, draw_number, routes and total_time, plus access_time, wait_time, ride_time, transfer_time, egress_time and n_rides when breakdown = TRUE. routes lists the public transport routes used, separated by |, or the street mode (e.g. ⁠[WALK]⁠) for trips that do not use public transport. draw_number only labels the draws of a departure minute; their order is arbitrary.

Travel times are reported with a precision of 0.1 minute, and trips longer than max_trip_duration get NA. travel_time_matrix() rounds travel times down to whole minutes, so it can report trips that are up to 0.9 minute over max_trip_duration which are NA here. With walk-, bike- or car-only mode, travel times are whole minutes.

If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

See Also

Other routing: arrival_travel_time_matrix(), detailed_itineraries(), pareto_frontier(), travel_time_matrix()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))

departure_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

# by default only returns the total time between each pair in each minute of
# the specified time window
ettm <- expanded_travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  time_window = 20,
  departure_datetime = departure_datetime,
  max_trip_duration = 60
)
head(ettm)

# when breakdown = TRUE the output contains much more information
ettm <- expanded_travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  time_window = 20,
  departure_datetime = departure_datetime,
  max_trip_duration = 60,
  breakdown = TRUE
)
head(ettm)

stop_r5(r5r_network)

Get most recent JAR file url from metadata

Description

Returns the most recent JAR file url from metadata, depending on the version.

Usage

fileurl_from_metadata(version = NULL)

Arguments

Value

A url a string.

See Also

Other support functions: exists_tiff(), get_gtfs_errors(), start_r5r_java(), stop_r5(), tempdir_unique(), travel_time_surface(), validate_bad_osm_ids()

Find snapped locations of input points on street network

Description

Finds the snapped location of points on R5 network. Snapping is an important step of the routing process, which is when the origins and destinations specified by the user are actually positioned on the network created by R5. The snapping process in R5 is composed of two rounds. First, it tries to snap the points within a radius of 300 meters from themselves. If the first round is unsuccessful, then R5 expands the search to the radius specified (by default 1.6km). If yet again it is unsuccessful, then the unsnapped points won't be used during the routing process. The snapped location of each point depends on the transport mode set by the user, because some network edges are not available to specific modes (e.g. a pedestrian-only street cannot be used to snap car trips).

Usage

find_snap(
  r5r_network,
  points,
  radius = 1600,
  mode = "WALK",
  r5r_core = deprecated()
)

Arguments

Value

A data.table with the original points, their respective snapped coordinates on the street network and the Euclidean distance (in meters) between the original points and their snapped location. Points that could not be snapped show NA coordinates and found = FALSE.

See Also

Other network functions: street_network_bbox(), street_network_to_sf(), transit_network_to_sf()

Examples

library(r5r)

path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path = path)
points <- read.csv(file.path(path, "poa_hexgrid.csv"))

snap_df <- find_snap(
  r5r_network,
  points = points,
  radius = 2000,
  mode = "WALK"
  )

stop_r5(r5r_network)

Get GTFS errors encountered in network building

Description

Returns a data frame of the GTFS errors R5 encountered when building the network. If the build failed and there is no network object, pass the path where the network is stored as r5r_network instead.

Usage

get_gtfs_errors(r5r_network)

Arguments

Value

A data.frame

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), start_r5r_java(), stop_r5(), tempdir_unique(), travel_time_surface(), validate_bad_osm_ids()

Examples

library(r5r)

# directory with street network and gtfs files
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

get_gtfs_errors(r5r_network)

Determine the Java version installed locally

Description

Determine the Java version installed locally

Usage

get_java_version()

Value

The number of the Java version

See Also

Other java support functions: dt_to_lts_map(), dt_to_speed_map(), java_to_dt(), run_routing_job()

Estimate isochrones from a given location

Description

Fast computation of isochrones from a given location. The function can return either polygon-based or line-based isochrones. Polygon-based isochrones are generated from a travel time surface: travel times from each origin to the centres of a regular grid of Web Mercator pixels (see zoom), from which the isochrone polygons are interpolated with the marching squares algorithm. Line-based isochrones are based on travel times from each origin to the centroids of all segments in the transport network.

Usage

isochrone(
  r5r_network,
  origins,
  mode = "transit",
  mode_egress = "walk",
  cutoffs = c(0, 15, 30),
  zoom = 10,
  departure_datetime = NULL,
  polygon_output = TRUE,
  time_window = 10L,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  draws_per_minute = 5L,
  seed = NULL,
  percentiles = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  sample_size = deprecated(),
  r5r_core = deprecated()
)

Arguments

Value

A ⁠"sf" "data.frame"⁠. With polygon_output = TRUE, one POLYGON or MULTIPOLYGON per origin, percentile and cutoff, with columns id (origin id), isochrone (cutoff in minutes), percentile (a string such as "p50") and polygons. Each polygon covers the whole area reached from 0 up to its cutoff, so polygons of larger cutoffs contain those of smaller ones. With polygon_output = FALSE, one LINESTRING per street segment reached, with columns id (origin id), edge_index, osm_id, isochrone (the smallest cutoff at or above the segment's travel time, i.e. bands are intervals), travel_time_p50 and geometry.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

Examples

old_options <- options(java.parameters = "-Xmx2G")
library(r5r)
library(ggplot2)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path = data_path)

# load origin/point of interest
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))
origin <- points[2,]

departure_datetime <- as.POSIXct(
 "13-05-2019 14:00:00",
 format = "%d-%m-%Y %H:%M:%S"
)

# estimate polygon-based isochrone from origin
iso_poly <- isochrone(
  r5r_network,
  origins = origin,
  mode = "walk",
  polygon_output = TRUE,
  departure_datetime = departure_datetime,
  cutoffs = seq(0, 120, 30)
  )

head(iso_poly)


# estimate line-based isochrone from origin
iso_lines <- isochrone(
  r5r_network,
  origins = origin,
  mode = "walk",
  polygon_output = FALSE,
  departure_datetime = departure_datetime,
  cutoffs = seq(0, 100, 25)
  )

head(iso_lines)


# plot colors
colors <- c('#ffe0a5','#ffcb69','#ffa600','#ff7c43','#f95d6a',
            '#d45087','#a05195','#665191','#2f4b7c','#003f5c')

# polygons
ggplot() +
  geom_sf(data=iso_poly, aes(fill=factor(isochrone))) +
  scale_fill_manual(values = colors) +
  theme_minimal()

# lines
ggplot() +
  geom_sf(data=iso_lines, aes(color=factor(isochrone))) +
  scale_color_manual(values = colors) +
  theme_minimal()

stop_r5(r5r_network)
options(old_options)

Java object to data.table

Description

Converts a Java object returned by r5r_network to an R data.table

Usage

java_to_dt(obj, ids = NULL)

Arguments

Value

An R data.table

See Also

Other java support functions: dt_to_lts_map(), dt_to_speed_map(), get_java_version(), run_routing_job()

Save LTS lines to shapefile temporary file

Description

Support function that checks the input of LTS lines passed to and saves it to a .shp temporary file.

Usage

lts_lines2shp(new_lts_lines)

Arguments

Value

The path to a .shp saved as a temporary file.

See Also

Other Support functions: congestion_poly2geojson()

Examples

# read lines with new speeds
new_lts_lines <- readRDS(
  system.file("extdata/poa/poa_ls_lts.rds", package = "r5r")
  )

shp_path <- r5r:::lts_lines2shp(
  new_lts_lines = new_lts_lines
  )

Calculate travel time and monetary cost Pareto frontier

Description

Fast computation of travel time and monetary cost Pareto frontier between origin and destination pairs.

Usage

pareto_frontier(
  r5r_network,
  origins,
  destinations,
  mode = c("WALK", "TRANSIT"),
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  seed = NULL,
  percentiles = 50L,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  fare_structure,
  fare_cutoffs,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A data.table with the travel time and monetary cost Pareto frontier between the specified origins and destinations. An additional column identifying the travel time percentile is present if more than one value was passed to percentiles. Origin and destination pairs whose trips couldn't be completed within the maximum travel time using less money than the specified monetary cutoffs are not returned in the data.table. If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The pareto_frontier() function, and detailed_itineraries() with shortest_path = FALSE or a fare_structure, use an R5-specific extension to the McRAPTOR routing algorithm. The implementation used in detailed_itineraries() allows the router to find paths that are optimal and less than optimal in terms of travel time, with some heuristics around multiple access modes, riding the same patterns, etc. The specific extension to McRAPTOR to do suboptimal path routing is not documented yet, but a detailed description of base McRAPTOR can be found in Delling et al (2015). The implementation used in pareto_frontier(), on the other hand, returns only the fastest trip within a given monetary cutoff, ignoring slower trips that cost the same. A detailed discussion on the algorithm can be found in Conway and Stewart (2019).

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

• Conway, M. W., & Stewart, A. F. (2019). Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics. International Journal of Geographical Information Science, 33(9), 1759-1787. doi:10.1080/13658816.2019.1605075

See Also

Other routing: arrival_travel_time_matrix(), detailed_itineraries(), expanded_travel_time_matrix(), travel_time_matrix()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path = data_path)

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_hexgrid.csv"))[1:5,]

# load fare structure object
fare_structure_path <- system.file(
  "extdata/poa/fares/fares_poa.zip",
  package = "r5r"
)
fare_structure <- read_fare_structure(fare_structure_path)

departure_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

pf <- pareto_frontier(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  fare_structure = fare_structure,
  fare_cutoffs = c(4.5, 4.8, 9, 9.3, 9.6)
)
head(pf)

stop_r5(r5r_network)

Manage cached files from the r5r package

Description

Manage cached files from the r5r package

Usage

r5r_cache(list_files = TRUE, delete_file = NULL)

Arguments

Value

A message indicating which files exist and/or which ones have been deleted from local cache directory.

Examples

# download r5 JAR
r5r::download_r5()

# list all files cached
r5r_cache(list_files = TRUE)

# delete r5 JAR
r5r_cache(delete_file = 'r5-v7.0')

Class to internally handle Java reference to R5RCore

Description

Class to internally handle Java reference to R5RCore

See Also

Other r5r_network: wrap_r5r_network()

Generate an r5r situation report to help debug errors

Description

The function reports a list with the following information:

• The package version of {r5r} in use.

• The installed version of R5.jar.

• The Java version in use.

• The amount of memory set to Java through the java.parameters option.

• The user's Session Info.

Usage

r5r_sitrep()

Value

A list with information of the versions of the r5r package, Java and R5 Jar in use, the memory set to Java and user's Session Info.

Examples

r5r_sitrep()

Read a fare structure object from a file

Description

Read a fare structure object from a file

Usage

read_fare_structure(file_path, encoding = "UTF-8")

Arguments

Value

A fare structure object.

See Also

Other fare structure: setup_fare_structure(), write_fare_structure()

Examples

path <- system.file("extdata/poa/fares/fares_poa.zip", package = "r5r")
fare_structure <- read_fare_structure(path)

Reverse BACK Origins and Destinations for Direct Modes

Description

Swaps BACK the origins and destinations data frames if they were originally reversed by reverse_if_direct_mode().

Usage

reverse_back_if_direct_mode(
  travel_times,
  origins,
  destinations,
  mode_list,
  data_path
)

Arguments

Value

A df travel time matrix with origins and destinations unchanged or in swapped order

See Also

Other setting functions: reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Reverse Origins and Destinations for Direct Modes

Description

Swaps the origins and destinations data frames if certain conditions are met, specifically to optimize routing performance with R5's one-to-many algorithm. The function reverses the direction of analysis when the transit mode is empty and the direct modes is WALK and when the number of origin points is greater than the number of destination points.

Usage

reverse_if_direct_mode(origins, destinations, mode_list, data_path)

Arguments

Value

List origins and destinations unchanged or in swapped order

See Also

Other setting functions: reverse_back_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Run a routing call on a Java background thread and print its progress

Description

Makes R5RCore start the routing method in call on a background thread, then polls it from R. This way the progress counter is printed by R, and shown in any console (Java's own output only reaches terminals), and the call can be interrupted. An interrupted job is cancelled by resetRoutingProperties(), which every routing function calls on exit. With options(r5r.async = FALSE) the call runs synchronously.

Usage

run_routing_job(r5r_network, call, progress)

Arguments

Value

The Java object returned by the routing method.

See Also

Other java support functions: dt_to_lts_map(), dt_to_speed_map(), get_java_version(), java_to_dt()

Set breakdown

Description

Sets whether travel time matrices should include detailed trip information or not.

Usage

set_breakdown(r5r_network, breakdown)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set cutoffs

Description

Sets the cutoffs used when calculating accessibility.

Usage

set_cutoffs(r5r_network, cutoffs, decay_function)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set elevation

Description

Verifies whether elevation mode is correct.

Usage

set_elevation(elevation)

Arguments

Value

Character. Correctly formatted elevation.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set expanded travel times

Description

Sets whether travel time matrices should return results for each minute of the specified time window.

Usage

set_expanded_travel_times(r5r_network, expanded)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set monetary cutoffs

Description

Sets the monetary cutoffs that should be considered when calculating the Pareto frontier.

Usage

set_fare_cutoffs(r5r_network, fare_cutoffs)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set the fare structure used when calculating transit fares

Description

Sets the fare structure used by our "generic" fare calculator. A value of NULL is passed to fare_structure by the upstream routing and accessibility functions when fares are not to be calculated.

Usage

set_fare_structure(r5r_network, fare_structure)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set max fare

Description

Sets the max fare allowed when calculating transit fares.

Usage

set_max_fare(r5r_network, max_fare, fare_structure)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set max Level of Transit Stress (LTS)

Description

Set max Level of Transit Stress (LTS)

Usage

set_max_lts(r5r_network, max_lts)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set max number of rides

Description

Sets the maximum number of rides a trip can use in R5.

Usage

set_max_rides(r5r_network, max_rides)

Arguments

Value

No return value, called for side effects.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set number of Monte Carlo draws

Description

Sets the number of Monte Carlo draws to be used by R5.

Usage

set_monte_carlo_draws(
  r5r_network,
  draws_per_minute,
  time_window,
  fare_structure = NULL,
  mcraptor = !is.null(fare_structure)
)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set the seed of the Monte Carlo draws

Description

Sets the seed used by R5 to draw the departure offsets of frequency-based transit routes.

Usage

set_monte_carlo_seed(r5r_network, seed)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set number of threads

Description

Sets the number of threads to be used by the r5r .jar.

Usage

set_n_threads(r5r_network, n_threads)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set car congestion

Description

Verifies if and which congestion mode to use and applies it.

Usage

set_new_congestion(r5r_network, new_carspeeds, carspeed_scale)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set LTS level

Description

Verifies if and which LTS mode to use and applies it.

Usage

set_new_lts(r5r_network, new_lts)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set output directory

Description

Sets whether r5r should save output to a specified directory.

Usage

set_output_dir(r5r_network, output_dir)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set percentiles

Description

Sets the percentiles to be used by R5.

Usage

set_percentiles(r5r_network, percentiles)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set progress argument

Description

Indicates whether or not a progress counter must be printed during computations. Applies to all routing functions.

Usage

set_progress(r5r_network, progress)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_speed(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set walk and bike speed

Description

This function receives the walk and bike 'speed' inputs in Km/h from routing functions above and converts them to meters per second, which is then used to set these speed profiles in r5r JAR.

Usage

set_speed(r5r_network, speed, mode)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_suboptimal_minutes(), set_time_window(), set_verbose()

Set suboptimal minutes

Description

Sets the number of suboptimal minutes considered in detailed_itineraries() routing. From R5 documentation: "This parameter compensates for the fact that GTFS does not contain information about schedule deviation (lateness). The min-max travel time range for some trains is zero, since the trips are reported to always have the same timings in the schedule. Such an option does not overlap (temporally) its alternatives, and is too easily eliminated by an alternative that is only marginally better. We want to effectively push the max travel time of alternatives out a bit to account for the fact that they don't always run on schedule".

Usage

set_suboptimal_minutes(
  r5r_network,
  suboptimal_minutes,
  fare_structure,
  shortest_path
)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_time_window(), set_verbose()

Set time window

Description

Sets the time window to be used by R5.

Usage

set_time_window(r5r_network, time_window)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_verbose()

Set verbose argument

Description

Indicates whether R5 should output informative messages or not. R5 error messages are still reported even when verbose is FALSE.

Usage

set_verbose(r5r_network, verbose)

Arguments

Value

Invisibly returns TRUE.

See Also

Other setting functions: reverse_back_if_direct_mode(), reverse_if_direct_mode(), set_breakdown(), set_cutoffs(), set_elevation(), set_expanded_travel_times(), set_fare_cutoffs(), set_fare_structure(), set_max_fare(), set_max_lts(), set_max_rides(), set_monte_carlo_draws(), set_monte_carlo_seed(), set_n_threads(), set_new_congestion(), set_new_lts(), set_output_dir(), set_percentiles(), set_progress(), set_speed(), set_suboptimal_minutes(), set_time_window()

Setup a fare structure to calculate the monetary costs of trips

Description

Creates a basic fare structure that describes how transit fares should be calculated in travel_time_matrix(), expanded_travel_time_matrix(), accessibility() and pareto_frontier(). This fare structure can be manually edited and adjusted to the existing rules in your study area, as long as they stick to some basic premises. See the fare-structure vignette.

Usage

setup_fare_structure(
  r5r_network,
  base_fare,
  by = "MODE",
  debug_path = NULL,
  debug_info = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A fare structure object.

See Also

Other fare structure: read_fare_structure(), write_fare_structure()

Examples

library(r5r)

data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

fare_structure <- setup_fare_structure(r5r_network, base_fare = 5)

# to debug fare calculation
fare_structure <- setup_fare_structure(
  r5r_network,
  base_fare = 5,
  debug_path = "fare_debug.csv",
  debug_info = "MODE"
)

fare_structure$debug_settings

# debugging can be manually turned off by setting output_file to ""
fare_structure$debug_settings <- ""

Create a transport network used for routing in R5 (deprecated)

Description

setup_r5() was renamed to build_network() to create a more consistent API. setup_r5() is being deprecated after r5r v2.3.0 and will be removed in a future release. Use build_network() instead.

Usage

setup_r5(
  data_path,
  verbose = FALSE,
  temp_dir = FALSE,
  elevation = "TOBLER",
  overwrite = FALSE
)

Arguments

Value

An r5r_network object, used by r5r's routing and support functions.

Elevation

More information about the TOBLER and MINETTI options to calculate the effects of elevation on travel times can be found in the references below:

• Campbell, M. J., et al (2019). Using crowdsourced fitness tracker data to model the relationship between slope and travel rates. Applied geography, 106, 93-107. doi:10.1016/j.apgeog.2019.03.008.

• Minetti, A. E., et al (2002). Energy cost of walking and running at extreme uphill and downhill slopes. Journal of applied physiology. doi:10.1152/japplphysiol.01177.2001.

• Tobler, W. (1993). Three presentations on geographical analysis and modeling: Non-isotropic geographic modeling speculations on the geometry of geography global spatial analysis. Technical Report. National center for geographic information and analysis. 93 (1). https://escholarship.org/uc/item/05r820mz.

See Also

Other Build network: build_network(), download_r5()

Examples

library(r5r)

# directory with street network and gtfs files
data_path <- system.file("extdata/poa", package = "r5r")

# `setup_r5()` has been deprecated, please switch to `build_network()`
r5r_network <- build_network(data_path)

Initialize Java and Check Version

Description

Sets up Java logging for r5r and ensures Java SE Development Kit 21 is installed.

Usage

start_r5r_java(data_path, temp_dir = FALSE, verbose = FALSE)

Arguments

Details

This function initializes the Java Virtual Machine (JVM) with a log path for r5r, and checks that the installed Java version is 21. If not, it stops with an informative error message and download links.

Value

No return value. The function will stop execution with an error if Java 21 is not found.

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), get_gtfs_errors(), stop_r5(), tempdir_unique(), travel_time_surface(), validate_bad_osm_ids()

Stop running r5r network

Description

Stops the given r5r_network objects, or all r5r_network objects in the calling environment if none is supplied.

Usage

stop_r5(...)

Arguments

Value

No return value, called for side effects.

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), get_gtfs_errors(), start_r5r_java(), tempdir_unique(), travel_time_surface(), validate_bad_osm_ids()

Examples

library(r5r)

path <- system.file("extdata/poa", package = "r5r")

r5r_network <- build_network(path)

stop_r5(r5r_network)

Extract the geographic bounding box of the transport network

Description

Extracts the geographic bounding box of the street network layer from a routable transport network built with build_network(). It is a fast and memory-efficient alternative to sf::st_bbox(street_network_to_sf(r5r_net)).

Usage

street_network_bbox(
  r5r_network,
  output = c("polygon", "bbox", "vector"),
  r5r_core = deprecated()
)

Arguments

Value

By default (output = "polygon"), an sf object with a single POLYGON geometry. If output = "bbox", an sf bbox object. If output = "vector", a named numeric vector with xmin, ymin, xmax, ymax coordinates. All outputs use the WGS84 coordinate reference system (EPSG: 4326).

See Also

Other network functions: find_snap(), street_network_to_sf(), transit_network_to_sf()

Examples

library(r5r)
library(sf)

data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# Get the network's bounding box as an sf polygon (default)
poly <- street_network_bbox(r5r_network, output = "polygon")
plot(poly)

# Get an sf bbox object (order is xmin, ymin, xmax, ymax)
box <- street_network_bbox(r5r_network , output = "bbox")
box

# Get a simple named vector (order now also xmin, ymin, xmax, ymax)
vec <- street_network_bbox(r5r_network , output = "vector")
vec

stop_r5(r5r_network)

Extract OpenStreetMap network in sf format

Description

Extracts the OpenStreetMap network in sf format from a routable transport network built with build_network().

Usage

street_network_to_sf(r5r_network, r5r_core = deprecated())

Arguments

Value

A list with two components of a street network in sf format: vertices (POINT) and edges (LINESTRING).

See Also

Other network functions: find_snap(), street_network_bbox(), transit_network_to_sf()

Examples

library(r5r)

# build transport network
path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(path)

# extract street network from r5r_network
street_net <- street_network_to_sf(r5r_network)

stop_r5(r5r_network)

Return a temporary directory path that is unique with every call

Description

This is different from the built in tempdir() in that it does not return the same directory within a given runtime. Always returns a unique directory

Usage

tempdir_unique()

Value

Path. Returns the path of the created temporary directory

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), get_gtfs_errors(), start_r5r_java(), stop_r5(), travel_time_surface(), validate_bad_osm_ids()

Extract transit network in sf format

Description

Extracts the transit network in sf format from a routable transport network built with build_network().

Usage

transit_network_to_sf(r5r_network, r5r_core = deprecated())

Arguments

Value

A list with two components of a transit network in sf format: route shapes (LINESTRING) and transit stops (POINT). The same route_id/short_name might appear with different geometries. This occurs when the same route is associated to more than one shape_ids in the GTFS feed used to create the transit network. Some transit stops might be returned with geometry ⁠POINT EMPTY⁠ (i.e. missing spatial coordinates). This may occur when a transit stop is not snapped to the road network, possibly because the GTFS feed used to create the transit network covers an area larger than the .osm.pbf input data.

See Also

Other network functions: find_snap(), street_network_bbox(), street_network_to_sf()

Examples

library(r5r)

# build transport network
path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(path)

# extract transit network from r5r_network
transit_net <- transit_network_to_sf(r5r_network)

stop_r5(r5r_network)

Calculate travel time matrix between origin destination pairs considering a departure time

Description

Fast computation of travel time estimates between one or multiple origin destination pairs. This function considers a departure time set by the user. If you want to calculate travel times considering a time of arrival, have a look at the arrival_travel_time_matrix() function.

Usage

travel_time_matrix(
  r5r_network,
  origins,
  destinations,
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  percentiles = 50L,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  fare_structure = NULL,
  max_fare = Inf,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  seed = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE,
  output_dir = NULL,
  r5r_core = deprecated()
)

Arguments

Value

A data.table with travel time estimates (in minutes) between origin and destination pairs. Pairs whose trips couldn't be completed within the maximum travel time and/or whose origin is too far from the street network are not returned in the data.table. If output_dir is not NULL, the function returns the path specified in that parameter, in which the .csv files containing the results are saved.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

See Also

Other routing: arrival_travel_time_matrix(), detailed_itineraries(), expanded_travel_time_matrix(), pareto_frontier()

Examples

library(r5r)

# build transport network
data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

# load origin/destination points
points <- read.csv(file.path(data_path, "poa_points_of_interest.csv"))

departure_datetime <- as.POSIXct(
  "13-05-2019 14:00:00",
  format = "%d-%m-%Y %H:%M:%S"
)

ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  max_trip_duration = 60
)
head(ttm)

# using a larger time window
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  time_window = 30,
  max_trip_duration = 60
)
head(ttm)

# selecting different percentiles
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  time_window = 30,
  percentiles = c(25, 50, 75),
  max_trip_duration = 60
)
head(ttm)

# use a fare structure and set a max fare to take monetary constraints into
# account
fare_structure <- read_fare_structure(
  file.path(data_path, "fares/fares_poa.zip")
)
ttm <- travel_time_matrix(
  r5r_network,
  origins = points,
  destinations = points,
  mode = c("WALK", "TRANSIT"),
  departure_datetime = departure_datetime,
  fare_structure = fare_structure,
  max_fare = 5,
  max_trip_duration = 60,
)
head(ttm)

stop_r5(r5r_network)

Compute travel time surfaces.

Description

A travel time surface is a raster grid (in the Web Mercator projection) containing travel times from a specified point.

Usage

travel_time_surface(
  r5r_network,
  origins,
  zoom = 10,
  mode = "WALK",
  mode_egress = "WALK",
  departure_datetime = NULL,
  time_window = 10L,
  percentiles = 50L,
  max_walk_time = Inf,
  max_bike_time = Inf,
  max_car_time = Inf,
  max_trip_duration = 120L,
  walk_speed = 3.6,
  bike_speed = 12,
  max_rides = 3,
  max_lts = 2,
  fare_structure = NULL,
  max_fare = Inf,
  new_carspeeds = NULL,
  carspeed_scale = 1,
  new_lts = NULL,
  draws_per_minute = 5L,
  seed = NULL,
  n_threads = Inf,
  verbose = FALSE,
  progress = TRUE
)

Arguments

Value

A ⁠"sf" "data.frame"⁠ for each isochrone of each origin.

Transport modes

R5 allows for multiple combinations of transport modes. The options include:

• Transit modes: TRAM, SUBWAY, RAIL, BUS, FERRY, CABLE_CAR, GONDOLA, FUNICULAR. The option TRANSIT automatically considers all public transport modes available.

• Non transit modes: WALK, BICYCLE, CAR.

Level of Traffic Stress (LTS)

When cycling is enabled in R5 (by passing the value BICYCLE to either mode or mode_egress), setting max_lts will allow cycling only on streets with a given level of danger/stress. Setting max_lts to 1, for example, will allow cycling only on separated bicycle infrastructure or low-traffic streets and routing will revert to walking when traversing any links with LTS exceeding 1. Setting max_lts to 3 will allow cycling on links with LTS 1, 2 or 3. Routing also reverts to walking if the street segment is tagged as non-bikable in OSM (e.g. a staircase), independently of the specified max LTS.

The default methodology for assigning LTS values to network edges is based on commonly tagged attributes of OSM ways. See more info about LTS in the original documentation of R5 from Conveyal at https://docs.conveyal.com/learn-more/traffic-stress. In summary:

• LTS 1: Tolerable for children. This includes low-speed, low-volume streets, as well as those with separated bicycle facilities (such as parking-protected lanes or cycle tracks).

• LTS 2: Tolerable for the mainstream adult population. This includes streets where cyclists have dedicated lanes and only have to interact with traffic at formal crossing.

• LTS 3: Tolerable for "enthused and confident" cyclists. This includes streets which may involve close proximity to moderate- or high-speed vehicular traffic.

• LTS 4: Tolerable only for "strong and fearless" cyclists. This includes streets where cyclists are required to mix with moderate- to high-speed vehicular traffic.

For advanced users, you can provide custom LTS values by adding a tag ⁠<key = "lts">⁠ to the osm.pbf file.

Datetime parsing

r5r ignores the timezone attribute of datetime objects when parsing dates and times, using the study area's timezone instead. For example, let's say you are running some calculations using Rio de Janeiro, Brazil, as your study area. The datetime as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") will be parsed as May 13th, 2019, 14:00h in Rio's local time, as expected. But as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S", tz = "Europe/Paris") will also be parsed as the exact same date and time in Rio's local time, perhaps surprisingly, ignoring the timezone attribute.

Routing algorithm

The travel_time_matrix(), expanded_travel_time_matrix(), arrival_travel_time_matrix() and accessibility() functions use an R5-specific extension to the RAPTOR routing algorithm (see Conway et al., 2017). This RAPTOR extension uses a systematic sample of one departure per minute over the time window set by the user in the 'time_window' parameter. A detailed description of base RAPTOR can be found in Delling et al (2015). However, whenever the user includes transit fares inputs to these functions, they automatically switch to use an R5-specific extension to the McRAPTOR routing algorithm. detailed_itineraries() also uses RAPTOR when it only returns the fastest itinerary (shortest_path = TRUE) and no fares are used.

• Conway, M. W., Byrd, A., & van der Linden, M. (2017). Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653(1), 45-53. doi:10.3141/2653-06

• Delling, D., Pajor, T., & Werneck, R. F. (2015). Round-based public transit routing. Transportation Science, 49(3), 591-604. doi:10.1287/trsc.2014.0534

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), get_gtfs_errors(), start_r5r_java(), stop_r5(), tempdir_unique(), validate_bad_osm_ids()

Validate OSM IDs returned from Java backend and print warnings

Description

Parses a Java-style array string (e.g., "[id1, id2]"), extracts OSM IDs, and prints a pretty warning if any invalid IDs are found.

Usage

validate_bad_osm_ids(bad_ids_string)

Arguments

Details

If no invalid IDs are found (i.e., input is "[]"), prints nothing.

Value

Warning if necessary.

See Also

Other support functions: exists_tiff(), fileurl_from_metadata(), get_gtfs_errors(), start_r5r_java(), stop_r5(), tempdir_unique(), travel_time_surface()

Constructor for r5r_network object

Description

Wraps a Java R5RCore as an r5r_network.

Usage

wrap_r5r_network(jcore)

Arguments

Value

r5r_network

See Also

Other r5r_network: r5r_network-class

Write a fare structure object to disk

Description

Writes a fare structure object to disk. Fare structure is saved as a collection of .csv files inside a .zip file.

Usage

write_fare_structure(fare_structure, file_path)

Arguments

Value

The path passed to file_path, invisibly.

See Also

Other fare structure: read_fare_structure(), setup_fare_structure()

Examples

library(r5r)

data_path <- system.file("extdata/poa", package = "r5r")
r5r_network <- build_network(data_path)

fare_structure <- setup_fare_structure(r5r_network, base_fare = 5)

tmpfile <- tempfile("sample_fare_structure", fileext = ".zip")
write_fare_structure(fare_structure, tmpfile)