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---
output: github_document
---
# osrm <img src="man/figures/logo.png" align="right" width="140"/>
[](https://cran.r-project.org/package=osrm)
[](https://cran.r-project.org/package=osrm)
[](https://github.com/riatelab/osrm/actions)
[](https://app.codecov.io/gh/riatelab/osrm)
[](https://www.repostatus.org/#active)
[](https://doi.org/10.21105/joss.04574)
***Interface between R and the OpenStreetMap-based routing service [OSRM](http://project-osrm.org/)***
## Description
OSRM is a routing service based on OpenStreetMap data. See <http://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 v6.0.0 of OSRM).
You can run your own instance of OSRM following guidelines provided [here](https://github.com/Project-OSRM/osrm-backend). A simple solution is to use [docker containers](https://github.com/Project-OSRM/osrm-backend#using-docker) and you can find and exemple of building a European-wide OSRM Server [here](https://rcarto.github.io/posts/build_osrm_server/).
Alternatively, you can use [`osrm.backend`](https://www.ekotov.pro/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](https://routing.openstreetmap.de/about.html) of the service**:
> [One request per second max. No scraping, no heavy usage.](https://routing.openstreetmap.de/about.html)
## Features
- `osrmTable()` uses the *table* service to query time/distance matrices,
- `osrmRoute()` uses the *route* service to query routes,
- `osrmTrip()` uses the *trip* service to query trips,
- `osrmNearest()` uses the *nearest* service to query the nearest point(s) on the street network,
- `osrmIsochrone()` and `osrmIsodistance()` use multiple `osrmTable()` calls to create isochrones or isodistances polygons.
## 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](https://www.openstreetmap.org/copyright/en)) stored in a [geopackage](https://www.geopackage.org/) 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.
``` r
library(osrm)
library(sf)
pharmacy <- st_read(system.file("gpkg/apotheke.gpkg", package = "osrm"),
quiet = TRUE)
travel_time <- osrmTable(loc = pharmacy)
travel_time$durations[1:5,1:5]
```
## 1 2 3 4 5
## 1 0.0 21.1 33.4 21.2 12.6
## 2 22.1 0.0 42.3 16.1 20.2
## 3 33.0 43.0 0.0 30.5 27.4
## 4 20.1 15.3 29.7 0.0 12.7
## 5 10.2 20.3 26.8 12.3 0.0
``` r
diag(travel_time$durations) <- NA
median(travel_time$durations, na.rm = TRUE)
```
## [1] 21.4
The median time needed to access any pharmacy from any other pharmacy is 21.4 minutes.
### Routes
`osrmRoute()` is used to compute the shortest route between two points. Here we compute the shortest route between the two first pharmacies.
``` r
(route <- osrmRoute(src = pharmacy[1, ], dst = pharmacy[2, ]))
```
## Simple feature collection with 1 feature and 4 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: -13177 ymin: 5837172 xmax: -3875.06 ymax: 5841047
## Projected CRS: WGS 84 / UTM zone 34N
## src dst duration distance geometry
## 1_2 1 2 21.68333 12.5251 LINESTRING (-13170.51 58410...
This route is 12.5 kilometers long and it takes 21.7 minutes to drive through it.
``` r
plot(st_geometry(route), main = "Route")
plot(st_geometry(pharmacy[1:2,]), pch = 20, add = T, cex = 1.5)
```
### Travelling salesman problem
`osrmTrip()` can be used to resolve the travelling salesman problem, it gives the shortest trip between a set of unordered points. In this example we want to obtain the shortest trip between the first five pharmacies.
``` r
(trips <- osrmTrip(loc = pharmacy[1:5, ], overview = "full"))
```
## [[1]]
## [[1]]$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 21.68333 12.5251 LINESTRING (-13170.77 58410...
## 2 2 4 16.26667 8.4495 LINESTRING (-3875.322 58379...
## 3 4 3 30.04667 18.1690 LINESTRING (-7444.513 58427...
## 4 3 5 27.85167 16.4466 LINESTRING (-8024.73 585621...
## 5 5 1 9.80000 4.2308 LINESTRING (-11716.82 58435...
##
## [[1]]$summary
## [[1]]$summary$duration
## [1] 105.6483
##
## [[1]]$summary$distance
## [1] 59.821
The shortest trip between these pharmacies takes 105.6 minutes and is 59.8 kilometers long. The steps of the trip are described in the "trip" sf object (point 1 > point 2 > point 4 > point 3 > point 5 > point 1).
``` r
par(mar = c(0,0,3,0))
mytrip <- trips[[1]]$trip
# Display the trip
plot(st_geometry(mytrip), col = c("black", "grey"), lwd = 2, main = "Trip")
plot(st_geometry(pharmacy[1:5, ]), cex = 1.5, pch = 21, add = TRUE)
text(st_coordinates(pharmacy[1:5,]), labels = row.names(pharmacy[1:5,]),
pos = 2)
```
### 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.
``` r
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
## loc loc 439 POINT (13.39671 52.46661)
The distance from the input point to the nearest point on the street network is of 439 meters
### 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.
``` r
(iso <- osrmIsochrone(loc = c(13.43,52.47), breaks = seq(0,12,2), n = 1000, smooth = F))
```
## Simple feature collection with 5 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 13.32727 ymin: 52.41842 xmax: 13.50226 ymax: 52.51358
## Geodetic CRS: WGS 84
## id isomin isomax geometry
## 1 1 0 4 MULTIPOLYGON (((13.4315 52....
## 2 2 4 6 MULTIPOLYGON (((13.44048 52...
## 3 3 6 8 MULTIPOLYGON (((13.44946 52...
## 4 4 8 10 MULTIPOLYGON (((13.4315 52....
## 5 5 10 12 MULTIPOLYGON (((13.44048 52...
``` r
bks <- sort(unique(c(iso$isomin, iso$isomax)))
pals <- hcl.colors(n = length(bks) - 1, palette = "Light Grays", rev = TRUE)
plot(iso["isomax"], breaks = bks, pal = pals,
main = "Isochrones (in minutes)", reset = FALSE)
points(x = 13.43, y = 52.47, pch = 4, lwd = 2, cex = 1.5)
```
## Installation
You can install the released version of `osrm` from [CRAN](https://CRAN.R-project.org/package=osrm) with:
``` r
install.packages("osrm")
```
Alternatively, you can install the development version of `osrm` from [r-universe](https://riatelab.r-universe.dev/osrm) with:
``` r
install.packages("osrm", repos = "https://riatelab.r-universe.dev")
```
## Community Guidelines
One can contribute to the package through [pull requests](https://github.com/riatelab/osrm/pulls) and report issues or ask questions [here](https://github.com/riatelab/osrm/issues). See the [CONTRIBUTING.md](https://github.com/riatelab/osrm/blob/master/CONTRIBUTING.md) file for detailed instructions.
## Acknowledgements
Many thanks to the editor (@elbeejay) and reviewers (@JosiahParry, @mikemahoney218 and @wcjochem) of the JOSS article.
This publication has led to a significant improvement in the code base and documentation of the package.