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GitHub - ipeaGIT/r5r

r5r: Rapid Realistic Routing with R5 in R

r5r is an R package for rapid realistic routing on multimodal transport networks (walk, bike, public transport and car). It provides a simple and friendly interface to R5, the Rapid Realistic Routing on Real-world and Reimagined networks, the routing engine developed independently by Conveyal.

r5r is a simple way to run R5 locally, allowing R users to generate detailed routing analysis or calculate travel time matrices and accessibility using seamless parallel computing. See a detailed demonstration of r5r in the intro Vignette. More details about r5r can be found on the package webpage or on this paper. Over time, r5r might be expanded to incorporate other functionality from R5.

This repository contains the R code (r-package folder) and the Java code (java-api folder) that provides the interface to R5.

You can install r5r:

# from CRAN
install.packages("r5r")

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

Please bear in mind that you need to have Java Development Kit (JDK) 21 installed on your computer to use r5r. No worries, you don't have to pay for it. There are numerous open-source JDK implementations, any of which should work with r5r. If you don't already have a preferred JDK, we recommend Adoptium/Eclipse Temurin. Other open-source JDK implementations include Amazon Corretto, and Oracle 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 21
rJavaEnv::java_quick_install(version = 21)

The package has seven fundamental functions :

  1. setup_r5()

  2. accessibility()

  3. travel_time_matrix()

  4. arrival_travel_time_matrix()

  5. expanded_travel_time_matrix()

  6. detailed_itineraries()

  7. pareto_frontier()

  8. isochrone()

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 in this vignette.

obs.2 The package also includes a convenient function modify_osm_carspeeds() that allows one build a routable network with modified OSM car speeds to account for different scenarios of traffic congestion and road closure. See this vignette.

The package also includes a few support functions.

  1. street_network_to_sf()

  2. transit_network_to_sf()

  3. find_snap()

  4. r5r_sitrep()

To use r5r, you will need:

Here are a few places from where you can download these data sets:

Demonstration on sample data

See a detailed demonstration of r5r in this intro Vignette. To illustrate functionality, the package includes a small sample data set of the public transport and Open Street Map networks of Porto Alegre (Brazil). Three steps are required to use r5r, as follows.

# allocate RAM memory to Java **before** loading the {r5r} library
options(java.parameters = "-Xmx2G")

library(r5r)

# 1) build transport network, pointing to the path where OSM and GTFS data are stored
path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path = path, verbose = FALSE)

# 2) load origin/destination points and set arguments
points <- read.csv(system.file("extdata/poa/poa_hexgrid.csv", package = "r5r"))
mode <- c("WALK", "TRANSIT")
max_walk_time <- 30   # minutes
max_trip_duration <- 60 # minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                 format = "%d-%m-%Y %H:%M:%S")

# 3.1) calculate a travel time matrix
ttm <- travel_time_matrix(r5r_core = r5r_core,
                          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 routes
det <- detailed_itineraries(r5r_core = r5r_core,
                            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 minutes 
access <- accessibility(r5r_core = r5r_core,
                        origins = points,
                        destinations = points,
                        opportunities_colname = "schools",
                        decay_function = "step",
                        cutoffs = 21,
                        mode =  c("WALK", "TRANSIT"),
                        verbose = FALSE)

There is a growing number of R packages with functionalities for transport routing, analysis and planning more broadly. Here are few of theses packages.

The r5r package is particularly focused on fast multimodal transport routing and accessibility. A key advantage of r5r is that is provides a simple and friendly R interface to R5, one of the fastest and most robust routing engines available.

For Python users, you might want to check our sister package: r5py!

The R5 routing engine is developed at Conveyal with contributions from several people.

Citation

The R package r5r is developed by a team at the Institute for Applied Economic Research (Ipea), Brazil. If you use this package in research publications, we please cite it as:

BibTeX:

@article{pereira_r5r_2021,
	title = {r5r: Rapid Realistic Routing on Multimodal Transport Networks with {R}$^{\textrm{5}}$ in R},
	shorttitle = {r5r},
	url = {https://findingspress.org/article/21262-r5r-rapid-realistic-routing-on-multimodal-transport-networks-with-r-5-in-r},
	doi = {10.32866/001c.21262},
	language = {en},
	urldate = {2021-03-04},
	journal = {Findings},
	author = {Pereira, Rafael H. M. and Saraiva, Marcus and Herszenhut, Daniel and Braga, Carlos Kaue Vieira and Conway, Matthew Wigginton},
	month = mar,
	year = {2021},
	note = {Publisher: Network Design Lab}
}

Please also cite the relevant publications relating to the Rāµ engine on which r5r builds up:

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