osrm

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Interface between R and the OpenStreetMap-based routing service OSRM

Description

OSRM is a routing service based on OpenStreetMap data. See https://project-osrm.org/ for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance) with R.

This package relies on the usage of a running OSRM service (tested with v26.10.0 of OSRM).

You can run your own instance of OSRM following guidelines provided here. A simple solution is to use docker containers and you can find and exemple of building a European-wide OSRM Server here.
Alternatively, you can use osrm.backend, an R package that installs and controls OSRM executables to prepare routing data and run/stop a local server.

⚠ You must be careful using the OSRM demo server and read the about page of the service:

One request per second max. No scraping, no heavy usage.

Features

Demo

This is a short overview of the main features of osrm. The dataset used here is shipped with the package, it is a sample of 100 random pharmacies in Berlin (© OpenStreetMap contributors) stored in a geopackage file.

Time / distance matrices

osrmTable() gives access to the table OSRM service. In this example we use this function to get the median time needed to access any pharmacy from any other pharmacy.

library(osrm)
library(sf)
library(mapsf)
pharmacy <- sf::st_read(system.file("gpkg/apotheke.gpkg", package = "osrm"), quiet = TRUE)
travel_time <- osrmTable(loc = pharmacy)
travel_time$durations[1:5, 1:5]
##      1    2    3    4    5
## 1  0.0 21.9 34.5 20.1 10.3
## 2 22.5  0.0 43.2 16.5 20.5
## 3 34.2 43.5  0.0 31.0 27.9
## 4 19.6 17.1 30.1  0.0 12.8
## 5  9.5 20.7 27.6 12.7  0.0
median(travel_time$durations)
## [1] 21.6

The median time needed to access any pharmacy from any other pharmacy is 21.6 minutes.

Routes

osrmRoute() is used to compute the shortest route between two points. Here we compute the shortest route between the two first pharmacies.

(route <- osrmRoute(src = pharmacy[1, ], dst = pharmacy[2, ]))
## Simple feature collection with 1 feature and 4 fields
## Geometry type: LINESTRING
## Dimension:     XY
## Bounding box:  xmin: -13170.51 ymin: 5837172 xmax: -3875.06 ymax: 5841047
## Projected CRS: WGS 84 / UTM zone 34N
##     src dst duration distance                       geometry
## 1_2   1   2     21.9   12.838 LINESTRING (-13170.51 58410...

This route is 12.838 kilometers long and it takes 21.9 minutes to drive through it.

mf_map(route, lwd = 2)
mf_map(pharmacy[1:2, ], pch = 20, cex = 1.5, add = TRUE)
mf_title("Route")

Travelling salesman problem

osrmTrip() can be used to resolve the travelling salesman problem, it gives the shortest trip between a set of unordered points. In this example we want to obtain the shortest trip between the first five pharmacies.

(trips <- osrmTrip(loc = pharmacy[1:5, ], overview = "full"))
## [[1]]
## [[1]]$trip
## Simple feature collection with 5 features and 4 fields
## Geometry type: LINESTRING
## Dimension:     XY
## Bounding box:  xmin: -13431.44 ymin: 5837172 xmax: -3875.322 ymax: 5856333
## Projected CRS: WGS 84 / UTM zone 34N
##   start end duration distance                       geometry
## 1     1   2     21.9   12.838 LINESTRING (-13170.77 58410...
## 2     2   4     16.5    8.450 LINESTRING (-3875.322 58379...
## 3     4   3     30.1   18.169 LINESTRING (-7444.513 58427...
## 4     3   5     27.9   16.447 LINESTRING (-8024.73 585621...
## 5     5   1      9.5    4.229 LINESTRING (-11716.82 58435...
## 
## [[1]]$summary
## [[1]]$summary$duration
## [1] 105.9
## 
## [[1]]$summary$distance
## [1] 60.133
## 
## 
## [[1]]$waypoints
## Simple feature collection with 5 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -13170.77 ymin: 5837935 xmax: -3875.322 ymax: 5856219
## Projected CRS: WGS 84 / UTM zone 34N
##   id snapping_distance                  geometry
## 1  1             0.003 POINT (-13170.77 5841047)
## 2  2             0.018 POINT (-3875.322 5837935)
## 4  4             0.027 POINT (-7444.513 5842729)
## 3  3             0.014  POINT (-8024.73 5856219)
## 5  5             0.007 POINT (-11716.82 5843569)

The shortest trip between these pharmacies takes 105.9 minutes and is 60.133 kilometers long. The steps of the trip are described in the “trip” sf object (point 1 > point 2 > point 4 > point 3 > point 5 > point 1).

trip <- trips[[1]]$trip
waypoints <- trips[[1]]$waypoints

# Display the trip
mf_map(trip, col = c("black", "grey"), lwd = 2)
mf_map(waypoints, cex = 1.5, pch = 21, add = TRUE)
mf_label(waypoints, var = "id", pos = 4, add = TRUE)
mf_title("Trip")

Point(s) on the street network

osrmNearest() returns the nearest point(s) on the street network from any point. Here we will get the nearest point on the network from a couple of coordinates.

pt_not_on_street_network <- c(13.40, 52.47)
(pt_on_street_network <- osrmNearest(loc = pt_not_on_street_network))
## Simple feature collection with 1 feature and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 13.39671 ymin: 52.46661 xmax: 13.39671 ymax: 52.46661
## Geodetic CRS:  WGS 84
##    id distance                  geometry
## 1 loc    0.439 POINT (13.39671 52.46661)

The distance from the input point to the nearest point on the street network is of 0.439 kilometers.

Isochrones

osrmIsochrone() computes areas that are reachable within a given time span from a point and returns the reachable regions as polygons. These areas of equal travel time are called isochrones. Here we compute the isochrones from a specific point defined by its longitude and latitude. Please note that in this example, we are using a local instance of the OSRM server to obtain very detailed isochrones (n = 20000).

iso <- osrmIsochrone(
  loc = c(13.43, 52.47),
  breaks = seq(0, 12, 1),
  n = 20000, smooth = TRUE,
  osrm.server = "http://0.0.0.0:5000/"
)
mf_map(iso, "isomax", "choro",
  breaks = unique(c(iso$isomin, iso$isomax)),
  leg_title = "Isochrones (in minutes)", leg_horiz = TRUE,
  leg_pos = "bottom", leg_val_rnd = 0
)
mf_title("Isochrones")

Installation

You can install the released version of osrm from CRAN with:

install.packages("osrm")

Alternatively, you can install the development version of osrm from r-universe with:

install.packages('osrm', repos = c('https://riatelab.r-universe.dev', 'https://cloud.r-project.org'))

Community Guidelines

One can contribute to the package through pull requests and report issues or ask questions here. See the CONTRIBUTING.md file for detailed instructions.

Acknowledgements

Many thanks to the editor and reviewers of the JOSS article.
This publication has led to a significant improvement in the code base and documentation of the package.