r5ris an R package for rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using 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 https://github.com/conveyal/r5
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 Java-based routing engine developed separately 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 ::install_github("ipeaGIT/r5r", subdir = "r-package")devtools
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
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
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
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
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.
<- system.file("extdata/poa", package = "r5r") data_path list.files(data_path) #>  "poa.zip" "poa_hexgrid.csv" #>  "poa_osm.pbf" "poa_points_of_interest.csv"
The points of interest data can be seen below. In this example, we will be looking at transport alternatives between some of those places.
<- fread(file.path(data_path, "poa_points_of_interest.csv")) poi head(poi) #> id lat lon #> 1: public_market -30.02756 -51.22781 #> 2: bus_central_station -30.02329 -51.21886 #> 3: gasometer_museum -30.03404 -51.24095 #> 4: santa_casa_hospital -30.03043 -51.22240 #> 5: townhall -30.02800 -51.22865 #> 6: piratini_palace -30.03363 -51.23068
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.
<- fread(file.path(data_path, "poa_hexgrid.csv")) points <- points[ c(sample(1:nrow(points), 10, replace=TRUE)), ] points head(points) #> id lon lat population schools #> 1: 89a90128c2fffff -51.24280 -30.03831 0 0 #> 2: 89a9012ae6bffff -51.23225 -30.07804 159 0 #> 3: 89a9012857bffff -51.21075 -30.07295 760 0 #> 4: 89a90129d27ffff -51.20266 -30.02371 785 0 #> 5: 89a9012a153ffff -51.23039 -30.10731 617 0 #> 6: 89a901291afffff -51.16122 -30.05220 833 0
The first step is to build the multimodal transport network used for
routing in R5. This is done with the
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 <- setup_r5(data_path = data_path, verbose = FALSE)r5r_core
For fast routing analysis, r5r currently has two
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
sf POINT object, or a
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 <- c("WALK", "TRANSIT") mode <- 5000 max_walk_dist <- 120 max_trip_duration <- as.POSIXct("13-05-2019 14:00:00", departure_datetime format = "%d-%m-%Y %H:%M:%S") # calculate a travel time matrix <- travel_time_matrix(r5r_core = r5r_core, ttm 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
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 <- poi[10,] origins <- poi[12,] destinations <- c("WALK", "TRANSIT") mode <- 10000 max_walk_dist <- as.POSIXct("13-05-2019 14:00:00", departure_datetime format = "%d-%m-%Y %H:%M:%S") # calculate detailed itineraries <- detailed_itineraries(r5r_core = r5r_core, dit 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
package: To provide a geographic context for the visualization of the
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_network_to_sf(r5r_core) street_net # 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) ::.jgc(R.gc = TRUE)rJava
If you have any suggestions or want to report an error, please visit the package GitHub page.