knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) # removes files created by 'setup_r5()' data_path <- system.file("extdata/poa", package = "r5r") existing_files <- list.files(data_path) files_to_keep <- c( "poa_hexgrid.csv", "poa_osm.pbf", "poa_points_of_interest.csv", "poa.zip" ) files_to_remove <- existing_files[! existing_files %in% files_to_keep] invisible(file.remove(file.path(data_path, files_to_remove)))
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, a really fast and open source routing engine based in
Java developed by Conveyal. R5 stands for Rapid Realistic Routing on Real-world and Reimagined networks.
r5r, you need to have Java SE Development Kit 11 installed on your computer. No worries, you don't have to pay for it. The jdk 11 is freely available from the options below:
You can install
r5r from CRAN, or the development version from github.
# CRAN install.packages('r5r') # github devtools::install_github("ipeaGIT/r5r", subdir = "r-package")
Before we start, we need to increase the memory available to Java. This is necessary because, by default,
R allocates only 512MB of memory for Java processes, which is not enough for large queries using
r5r. To increase available memory to 2GB, for example, we need to set the
java.parameters option at the beginning of the script, as follows:
options(java.parameters = "-Xmx2G")
Note: It's very important to allocate enough memory before attaching
r5r or any other Java-based package, since
rJava starts a Java Virtual Machine only once for each R session. It might be useful to restart your R session and execute the code above right after, if you notice that you haven't succeeded in your previous attempts.
Then we can load the packages used in this vignette:
library(r5r) library(sf) library(data.table) library(ggplot2) library(mapview) mapviewOptions(platform = 'leafgl')
r5r package has 3 fundamental functions.
setup_r5() to initialize an instance of
r5r, that also builds a routable transport network given an Open Street Map street network and public transport feeds in GTFS format;
travel_time_matrix() for fast computation of travel time estimates between origin/destination pairs;
detailed_itineraries() to get detailed information on one or multiple alternative routes between origin/destination pairs.
Let's have a quick look at how these functions work using a sample data set.
To illustrate functionality, the package includes a small sample data for the city of Porto Alegre (Brazil). It includes four files:
.csvformat, containing the names and spatial coordinates of 15 places within Porto Alegre;
.csvfile, which can be used as origin/destination pairs in a travel time matrix calculation.
data_path <- system.file("extdata/poa", package = "r5r") list.files(data_path)
The points of interest data can be seen below. In this example, we will be looking at transport alternatives between some of those places.
poi <- fread(file.path(data_path, "poa_points_of_interest.csv")) head(poi)
The data with origin destination pairs is shown below. In this example, we will be building a travel time matrix between ten random points in this data set.
points <- fread(file.path(data_path, "poa_hexgrid.csv")) points <- points[ c(sample(1:nrow(points), 10, replace=TRUE)), ] head(points)
The first step is to build the multimodal transport network used for routing in R5. This is done with the
setup_r5 function. This function does two things: (1) downloads/updates a compiled JAR file of R5 and stores it locally in the
r5r package directory for future use; and (2) combines the osm.pbf and gtfs.zip data sets to build a routable network object.
# Indicate the path where OSM and GTFS data are stored r5r_core <- setup_r5(data_path = data_path, verbose = FALSE)
For fast routing analysis, r5r currently has two core functions:
travel_time_matrix function is a really simple and fast function to compute travel time estimates between one or multiple origin/destination pairs. The origin/destination input can be either a spatial
sf POINT object, or a
data.frame containing the columns
id, lon, lat. The function also receives as inputs the max walking distance, in meters, and the max trip duration, in minutes. Resulting travel times are also output in minutes.
# set inputs mode <- c("WALK", "TRANSIT") max_walk_dist <- 5000 max_trip_duration <- 120 departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") # 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_dist = max_walk_dist, max_trip_duration = max_trip_duration, verbose = FALSE) head(ttm)
Most routing packages only return the fastest route. A key advantage of the
detailed_itineraries function is that is allows for fast routing analysis while providing multiple alternative routes between origin/destination pairs. The output also brings detailed information for each route alternative at the trip segment level, including the transport mode, waiting times, travel time and distance of each trip segment.
In this example below, we want to know some alternative routes between one origin/destination pair only.
# set inputs origins <- poi[10,] destinations <- poi[12,] mode <- c("WALK", "TRANSIT") max_walk_dist <- 10000 departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") # calculate detailed itineraries dit <- detailed_itineraries(r5r_core = r5r_core, origins = origins, destinations = destinations, mode = mode, departure_datetime = departure_datetime, max_walk_dist = max_walk_dist, shortest_path = FALSE, verbose = FALSE) head(dit)
The output is a
data.frame sf object, so we can easily visualize the results.
Static visualization with
ggplot2 package: To provide a geographic context for the visualization of the results in
ggplot2, you can also use the
street_network_to_sf function to extract the OSM street network used in the routing.
# extract OSM network street_net <- street_network_to_sf(r5r_core) # plot ggplot() + geom_sf(data = street_net$edges, color='gray85') + geom_sf(data = dit, aes(color=mode)) + facet_wrap(.~option) + theme_void()
Interactive visualization with
mapview(dit, zcol = 'option')
r5r objects are still allocated to any amount of memory previously set after they are done with their calculations. In order to remove an existing
r5r object and reallocate the memory it had been using, we use the
stop_r5 function followed by a call to Java's garbage collector, as follows:
stop_r5(r5r_core) rJava::.jgc(R.gc = TRUE)
If you have any suggestions or want to report an error, please visit the package GitHub page.
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