r5r: Rapid Realistic Routing with R5 in R

Installation

You can installr5r:

# from CRANinstall.packages("r5r")# dev version with latest featuresutils::remove.packages('r5r')devtools::install_github("ipeaGIT/r5r", subdir="r-package")

Please bear in mind that you need to haveJava Development Kit (JDK) 21 installed on your computer to user5r. No worries, you don’t have to pay for it. There are numerous open-source JDK implementations, any of which should work withr5r. If you don’t already have a preferred JDK, we recommendAdoptium/Eclipse Temurin. Other open-source JDK implementations includeAmazon Corretto, andOracle OpenJDK. You only need to install one JDK.

The easiest way to install JDK is using the new{rJavaEnv} package in R:

# install.packages('rJavaEnv')# check version of Java currently installed (if any)rJavaEnv::java_check_version_rjava()# install Java 21rJavaEnv::java_quick_install(version=21)

Usage

The package has sevenfundamental functions :

1. build_network()
   • Downloads and stores locally an R5 Jar file (the Jar file is downloaded only once per installation)
   • Builds a multimodal transport network given (1) a OpenStreetMap street network in.pbf format (mandatory), (2) one or more public transport networks inGTFS.zip format (optional), and (3) elevation data inraster.tif (optional).
2. accessibility()
   • Fast computation of access to opportunities. The function returns adata.table with accessibility estimates for all origin points by transport mode given a selected decay function. Multiple decay functions are available, including step (cumulative opportunities), logistic, fixed exponential and linear.
3. travel_time_matrix()
   • Fast function that returns a simpledata.table with travel time estimates between one or multiple origin destination pairs considering departure time.
4. arrival_travel_time_matrix()
   • Calculates travel time matrices between origin destination pairs considering a time of arrival. The output includes additional information such as the routes used and total time disaggregated by access, waiting, in-vehicle and transfer times.
5. expanded_travel_time_matrix()
   • Calculates travel time matrices between origin destination pairs with additional information such as routes used and total time disaggregated by access, waiting, in-vehicle and transfer times.
6. detailed_itineraries()
   • Returns adata.frame sf LINESTRINGs with one or multiple alternative routes between one or multiple origin destination pairs. The data output brings detailed information on transport mode, travel time, walk distance etc for each trip segment.
7. pareto_frontier()
   • Returns adata.table with the travel time and monetary cost of multiple route alternatives for specified origin-destination pairs.
8. isochrone()
   • Returns asf" "data.frame" showing the area that can be reached from an origin point at a given travel time limit.

See more details about each function here

obs. Most of these functions also allow users to account for monetary travel costs when generating travel time matrices and accessibility estimates. More info on how to consider monetary costs can be found inthis vignette.

The package also includes a fewsupport functions.

1. street_network_to_sf()
   • Extract OpenStreetMap network in sf format from anetwork.dat file.
2. transit_network_to_sf()
   • Extract transit network in sf format from anetwork.dat file.
3. find_snap()
   • Find snapped locations of input points on street network.
4. r5r_sitrep()
   • Generate a situation report to help debug eventual errors.

# allocate RAM memory to Java **before** loading the {r5r} libraryoptions(java.parameters="-Xmx2G")library(r5r)# 1) build transport network, pointing to the path where OSM and GTFS data are storedpath<-system.file("extdata/poa", package="r5r")r5r_network<-build_network(data_path=path, verbose=FALSE)# 2) load origin/destination points and set argumentspoints<-read.csv(system.file("extdata/poa/poa_hexgrid.csv", package="r5r"))mode<-c("WALK","TRANSIT")max_walk_time<-30# minutesmax_trip_duration<-60# minutesdeparture_datetime<-as.POSIXct("13-05-2019 14:00:00",                                 format="%d-%m-%Y %H:%M:%S")# 3.1) calculate a travel time matrixttm<-travel_time_matrix(r5r_network=r5r_network,                          origins=points,                          destinations=points,                          mode=mode,                          departure_datetime=departure_datetime,                          max_walk_time=max_walk_time,                          max_trip_duration=max_trip_duration)# 3.2) or get detailed info on multiple alternative routesdet<-detailed_itineraries(r5r_network=r5r_network,                            origins=points[370,],                            destinations=points[200,],                            mode=mode,                            departure_datetime=departure_datetime,                            max_walk_time=max_walk_time,                            max_trip_duration=max_trip_duration,                            shortest_path=FALSE,                            drop_geometry=FALSE)# 4) Calculate number of schools accessible within 20 minutesaccess<-accessibility(r5r_network=r5r_network,                        origins=points,                        destinations=points,                        opportunities_colname="schools",                        decay_function="step",                        cutoffs=21,                        mode=c("WALK","TRANSIT"),                        verbose=FALSE)