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. More details about r5r can be found on the package webpage or on this paper.
You can install
r5r from CRAN, or the development version from github.
# CRAN install.packages('r5r') # dev version on github devtools::install_github("ipeaGIT/r5r", subdir = "r-package")
Please bear in mind that you need to have Java SE Development Kit 11 installed on your computer to use
r5r. No worries, you don’t have to pay for it. The jdk 11 is freely available from the options below:
If you don’t know what version of Java you have installed on your computer, you can check it by running this on R console.
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 loading
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:
r5r package has six fundamental functions:
setup_r5() to initialize an instance of
r5r, that also builds a routable transport network;
accessibility() for fast computation of access to opportunities considering a selected decay function;
travel_time_matrix() for fast computation of travel time estimates between origin/destination pairs;
expanded_travel_time_matrix() for calculating travel matrices between origin destination pairs with additional information such routes used and total time disaggregated by access, waiting, in-vehicle and transfer times.
detailed_itineraries() to get detailed information on one or multiple alternative routes between origin/destination pairs.
pareto_frontier() for analyzing the trade-off between the travel time and monetary costs of multiple route alternatives between origin/destination pairs.
Most of these functions also allow users to account for monetary travel costs when generating travel time matrices and accessibility estimates. More info about how to consider monetary costs can be found in this vignette.
The package also includes a few support functions.
street_network_to_sf() to extract OpenStreetMap network in sf format from a
transit_network_to_sf() to extract transit network in sf format from a
find_snap() to find snapped locations of input points on street network.
r5r_sitrep() to generate a situation report to help debug eventual errors.
r5r, you will need: - A road network data set from OpenStreetMap in
.pbf format (mandatory) - A public transport feed in
GTFS.zip format (optional) - A raster file of Digital Elevation Model data in
.tif format (optional)
Here are a few places from where you can download these data sets:
Let’s have a quick look at how
r5r works using a sample data set.
To illustrate the functionalities of
r5r, the package includes a small sample data for the city of Porto Alegre (Brazil). It includes seven files:
poa_hexgrid.csvfile with spatial coordinates of a regular hexagonal grid covering the sample area, which can be used as origin/destination pairs in a travel time matrix calculation.
poa_points_of_interest.csvfile containing the names and spatial coordinates of 15 places within Porto Alegre
fares_poa.zipfile with the fare rules of the city’s public transport system.
data_path <- system.file("extdata/poa", package = "r5r") list.files(data_path) #>  "fares" "network_settings.json" #>  "network.dat" "poa_elevation.tif" #>  "poa_eptc.zip" "poa_hexgrid.csv" #>  "poa_osm.pbf" "poa_osm.pbf.mapdb" #>  "poa_osm.pbf.mapdb.p" "poa_points_of_interest.csv" #>  "poa_trensurb.zip"
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) #> 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 using 200 points randomly selected from this data set.
points <- fread(file.path(data_path, "poa_hexgrid.csv")) # sample points sampled_rows <- sample(1:nrow(points), 200, replace=TRUE) points <- points[ sampled_rows, ] head(points) #> id lon lat population schools jobs healthcare #> 1: 89a90128b4fffff -51.22624 -30.00504 49 0 3 0 #> 2: 89a9012880bffff -51.23320 -30.02665 2 0 0 0 #> 3: 89a90128017ffff -51.19851 -30.07122 855 0 13 0 #> 4: 89a9012802fffff -51.20115 -30.06129 602 1 105 0 #> 5: 89a9012d6d3ffff -51.25060 -30.00573 0 0 0 0 #> 6: 89a901286a3ffff -51.18374 -30.08219 34 0 0 0
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)
The faster way to calculate accessibility estimates is using the
accessibility() function. In this example, we calculate the number of schools and health care facilities accessible in less than 60 minutes by public transport and walking. More details in this vignette on Calculating and visualizing Accessibility.
# set departure datetime input departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") # calculate accessibility access <- accessibility(r5r_core = r5r_core, origins = points, destinations = points, opportunities_colnames = c("schools", "healthcare"), mode = c("WALK", "TRANSIT"), departure_datetime = departure_datetime, decay_function = "step", cutoffs = 60 ) head(access) #> id opportunity percentile cutoff accessibility #> 1: 89a90128b4fffff schools 50 60 0 #> 2: 89a90128b4fffff healthcare 50 60 2 #> 3: 89a9012880bffff schools 50 60 28 #> 4: 89a9012880bffff healthcare 50 60 21 #> 5: 89a90128017ffff schools 50 60 29 #> 6: 89a90128017ffff healthcare 50 60 23
For fast routing analysis, r5r currently has three 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.
This function also allows users to very efficiently capture the travel time uncertainties inside a given time window considering multiple departure times. More info on this vignette.
# set inputs mode <- c("WALK", "TRANSIT") max_walk_time <- 30 # minutes max_trip_duration <- 120 # minutes 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 = poi, destinations = poi, mode = mode, departure_datetime = departure_datetime, max_walk_time = max_walk_time, max_trip_duration = max_trip_duration) head(ttm) #> from_id to_id travel_time_p50 #> 1: public_market public_market 0 #> 2: public_market bus_central_station 13 #> 3: public_market gasometer_museum 10 #> 4: public_market santa_casa_hospital 15 #> 5: public_market townhall 3 #> 6: public_market piratini_palace 17
For those interested in more detailed outputs, the
expanded_travel_time_matrix() works very similarly with
travel_time_matrix() but it brings much more information. It estimates for each origin destination pair the routes used and total time disaggregated by access, waiting, in-vehicle and transfer times. Please note this function can be very memory intensive for large data sets.
# calculate a travel time matrix ettm <- expanded_travel_time_matrix(r5r_core = r5r_core, origins = poi, destinations = poi, mode = mode, departure_datetime = departure_datetime, breakdown = TRUE, max_walk_time = max_walk_time, max_trip_duration = max_trip_duration) head(ettm) #> from_id to_id departure_time draw_number access_time #> 1: public_market public_market 14:00:00 1 0.0 #> 2: public_market bus_central_station 14:00:00 1 2.6 #> 3: public_market gasometer_museum 14:00:00 1 2.9 #> 4: public_market santa_casa_hospital 14:00:00 1 0.0 #> 5: public_market townhall 14:00:00 1 0.0 #> 6: public_market piratini_palace 14:00:00 1 2.9 #> wait_time ride_time transfer_time egress_time routes n_rides total_time #> 1: 0.0 0.0 0 0.0 [WALK] 0 0.0 #> 2: 2.4 1.5 0 7.4 D73 1 13.9 #> 3: 1.1 4.5 0 1.8 2821 1 10.3 #> 4: 0.0 0.0 0 0.0 [WALK] 0 15.3 #> 5: 0.0 0.0 0 0.0 [WALK] 0 3.5 #> 6: 1.1 1.3 0 11.8 2821 1 17.1
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_time <- 60 # minutes departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") # calculate detailed itineraries det <- detailed_itineraries(r5r_core = r5r_core, origins = origins, destinations = destinations, mode = mode, departure_datetime = departure_datetime, max_walk_time = max_walk_time, shortest_path = FALSE) head(det) #> Simple feature collection with 5 features and 16 fields #> Geometry type: LINESTRING #> Dimension: XY #> Bounding box: xmin: -51.24094 ymin: -30.05 xmax: -51.19762 ymax: -29.99729 #> Geodetic CRS: WGS 84 #> from_id from_lat from_lon to_id to_lat #> 1 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995 #> 2 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995 #> 3 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995 #> 4 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995 #> 5 farrapos_station -29.99772 -51.19762 praia_de_belas_shopping_center -30.04995 #> to_lon option departure_time total_duration total_distance segment mode #> 1 -51.22875 1 14:00:54 44.4 9460 1 WALK #> 2 -51.22875 1 14:00:54 44.4 9460 2 RAIL #> 3 -51.22875 1 14:00:54 44.4 9460 3 WALK #> 4 -51.22875 1 14:00:54 44.4 9460 4 BUS #> 5 -51.22875 1 14:00:54 44.4 9460 5 WALK #> segment_duration wait distance route geometry #> 1 4.6 0.0 174 LINESTRING (-51.1981 -29.99... #> 2 6.6 9.5 4796 LINHA1 LINESTRING (-51.19763 -29.9... #> 3 5.7 0.0 256 LINESTRING (-51.22827 -30.0... #> 4 10.4 4.4 4083 188 LINESTRING (-51.22926 -30.0... #> 5 3.2 0.0 151 LINESTRING (-51.22949 -30.0...
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) # extract public transport network transit_net <- r5r::transit_network_to_sf(r5r_core) # plot ggplot() + geom_sf(data = street_net$edges, color='gray85') + geom_sf(data = det, aes(color=mode)) + facet_wrap(.~option) + theme_void()
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:
If you have any suggestions or want to report an error, please visit the package GitHub page.