Intro to r5r: Rapid Realistic Routing with R5 in R

Abstract

r5r is 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

1. Introduction

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.

2. Installation

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: - OpenJDK - Oracle

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.

  rJava::.jinit()
  rJava::.jcall("java.lang.System", "S", "getProperty", "java.version")

3. Usage

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:

library(r5r)
library(sf)
library(data.table)
library(ggplot2)

The r5r package has seven 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 as 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.
isochrone() to estimate the polygons of the areas that can be reached from an origin point at different travel time limits.

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 network.dat file.
transit_network_to_sf() to extract transit network in sf format from a network.dat file.
find_snap() to find snapped locations of input points on street network.
r5r_sitrep() to generate a situation report to help debug eventual errors.

3.1 Data requirements:

To use 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:

OpenStreetMap
- osmextract R package
- geofabrik website
- hot export tool website
- BBBike.org website
- Protomaps website
GTFS
- tidytransit R package
- transitland website
- Mobility Database website
Elevation
- elevatr R package
- Nasa’s SRTMGL1 website

Let’s have a quick look at how r5r works using a sample data set.

4. Demonstration on sample data

Data

To illustrate the functionalities of r5r, the package includes a small sample data for the city of Porto Alegre (Brazil). It includes seven files:

An OpenStreetMap network: poa_osm.pbf
Two public transport feeds: poa_eptc.zip and poa_trensurb.zip
A raster elevation data: poa_elevation.tif
A poa_hexgrid.csv file 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.
A poa_points_of_interest.csv file containing the names and spatial coordinates of 15 places within Porto Alegre
A fares_poa.zip file with the fare rules of the city’s public transport system.

data_path <- system.file("extdata/poa", package = "r5r")
list.files(data_path)
#>  [1] "fares"                      "network_settings.json"     
#>  [3] "network.dat"                "poa_elevation.tif"         
#>  [5] "poa_eptc.zip"               "poa_hexgrid.csv"           
#>  [7] "poa_osm.pbf"                "poa_osm.pbf.mapdb"         
#>  [9] "poa_osm.pbf.mapdb.p"        "poa_points_of_interest.csv"
#> [11] "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: 89a9012846bffff -51.18980 -30.08167         93       0    1          0
#> 2: 89a90128377ffff -51.19441 -30.04522       1333       0   83          0
#> 3: 89a90128a43ffff -51.20553 -30.01930       1525       0  678          0
#> 4: 89a90129b7bffff -51.16516 -29.99914          0       0   78          0
#> 5: 89a90129d03ffff -51.19385 -30.03139        602       0  207          0
#> 6: 89a90128ccbffff -51.22815 -30.05205       1026       0  293          0

4.1 Building routable transport network with `setup_r5()`

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)

4.2 Accessibility analysis

The fastest 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: 89a9012846bffff     schools         50     60            16
#> 2: 89a9012846bffff  healthcare         50     60            13
#> 3: 89a90128377ffff     schools         50     60            32
#> 4: 89a90128377ffff  healthcare         50     60            24
#> 5: 89a90128a43ffff     schools         50     60            27
#> 6: 89a90128a43ffff  healthcare         50     60            21

4.3 Routing analysis

For fast routing analysis, r5r currently has three core functions: travel_time_matrix(), expanded_travel_time_matrix() and detailed_itineraries().

Fast many to many travel time matrix

The 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              14
#> 3: public_market    gasometer_museum              12
#> 4: public_market santa_casa_hospital              15
#> 5: public_market            townhall               3
#> 6: public_market     piratini_palace              17

Expanded travel time matrix with minute-by-minute estimates

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 wait_time
#> 1: public_market public_market       14:00:00           1           0         0
#> 2: public_market public_market       14:01:00           1           0         0
#> 3: public_market public_market       14:02:00           1           0         0
#> 4: public_market public_market       14:03:00           1           0         0
#> 5: public_market public_market       14:04:00           1           0         0
#> 6: public_market public_market       14:05:00           1           0         0
#>    ride_time transfer_time egress_time routes n_rides total_time
#> 1:         0             0           0 [WALK]       0          0
#> 2:         0             0           0 [WALK]       0          0
#> 3:         0             0           0 [WALK]       0          0
#> 4:         0             0           0 [WALK]       0          0
#> 5:         0             0           0 [WALK]       0          0
#> 6:         0             0           0 [WALK]       0          0

Detailed itineraries

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 6 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
#> 6 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       14:09:10           36.2           9460       1 WALK
#> 2 -51.22875      1       14:09:10           36.2           9460       2 RAIL
#> 3 -51.22875      1       14:09:10           36.2           9460       3 WALK
#> 4 -51.22875      1       14:09:10           36.2           9460       4  BUS
#> 5 -51.22875      1       14:09:10           36.2           9460       5 WALK
#> 6 -51.22875      2       14:09:43           48.7           8779       1 WALK
#>   segment_duration wait distance  route                       geometry
#> 1              4.6  0.0      174        LINESTRING (-51.1981 -29.99...
#> 2              6.6  1.3     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...
#> 6              4.6  0.0      174        LINESTRING (-51.1981 -29.99...

The output is a data.frame sf object, so we can easily visualize the results.

Visualize 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()

Cleaning up after usage

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:

r5r::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.

Rafael H. M. Pereira, Marcus Saraiva, Daniel Herszenhut, Carlos Kaue Braga

2023-09-09

1. Introduction

2. Installation

3. Usage

3.1 Data requirements:

4. Demonstration on sample data

Data

4.1 Building routable transport network with `setup_r5()`

4.2 Accessibility analysis

4.3 Routing analysis

Fast many to many travel time matrix

Expanded travel time matrix with minute-by-minute estimates

Detailed itineraries

Visualize results

Cleaning up after usage

Intro to r5r: Rapid Realistic Routing with R5 in R

Rafael H. M. Pereira, Marcus Saraiva, Daniel Herszenhut, Carlos Kaue Braga

2023-09-09

1. Introduction

2. Installation

3. Usage

3.1 Data requirements:

4. Demonstration on sample data

Data

4.1 Building routable transport network with setup_r5()

4.2 Accessibility analysis

4.3 Routing analysis

Fast many to many travel time matrix

Expanded travel time matrix with minute-by-minute estimates

Detailed itineraries

Visualize results

Cleaning up after usage

4.1 Building routable transport network with `setup_r5()`