# OSRM — High-Performance Open Source Routing Engine

> OSRM computes shortest paths on road networks using OpenStreetMap data, delivering sub-second route calculations for driving, cycling, and walking.

## Install

Save in your project root:

# OSRM — High-Performance Open Source Routing Engine

## Quick Use
```bash
# Download and preprocess OpenStreetMap data
wget https://download.geofabrik.de/europe/germany-latest.osm.pbf
docker run -t -v $(pwd):/data ghcr.io/project-osrm/osrm-backend osrm-extract -p /opt/car.lua /data/germany-latest.osm.pbf
docker run -t -v $(pwd):/data ghcr.io/project-osrm/osrm-backend osrm-partition /data/germany-latest.osrm
docker run -t -v $(pwd):/data ghcr.io/project-osrm/osrm-backend osrm-customize /data/germany-latest.osrm
docker run -t -p 5000:5000 -v $(pwd):/data ghcr.io/project-osrm/osrm-backend osrm-routed --algorithm mld /data/germany-latest.osrm
```
Query: `curl "http://localhost:5000/route/v1/driving/13.388860,52.517037;13.397634,52.529407"`

## Introduction
OSRM (Open Source Routing Machine) is a C++ routing engine that computes fastest paths on road networks derived from OpenStreetMap. It preprocesses map data into a compact graph representation, enabling sub-second responses for route, table, match, trip, and nearest queries.

## What OSRM Does
- Computes point-to-point routes with turn-by-turn instructions
- Generates distance and duration matrices for many-to-many queries
- Matches GPS traces to road segments via map matching
- Solves traveling salesman approximations with the trip service
- Finds nearest road segments for snapping coordinates to the network

## Architecture Overview
OSRM uses a multi-level Dijkstra (MLD) or contraction hierarchies (CH) algorithm. During preprocessing, the road graph is extracted from OSM PBF files using Lua profiles that define vehicle-specific speeds and restrictions. The preprocessed data is loaded into memory by the HTTP server (osrm-routed), which handles concurrent requests via a thread pool and responds with JSON including geometry, duration, and step-by-step instructions.

## Self-Hosting & Configuration
- Run with Docker using the official `ghcr.io/project-osrm/osrm-backend` image
- Download region-specific OSM extracts from Geofabrik or planet.openstreetmap.org
- Customize routing behavior by editing Lua profiles for car, bicycle, or foot
- Use MLD algorithm for faster preprocessing and comparable query speed to CH
- Deploy behind a reverse proxy with caching for production workloads

## Key Features
- Sub-second response times even for cross-country routes on a single machine
- HTTP API compatible with the OSRM API v1 specification used by many frontends
- Lua-scripted profiles allow customizing speeds, turn penalties, and access restrictions
- Supports alternative routes, waypoints, and route annotations (speed, duration per segment)
- Compact preprocessed data fits continental datasets in moderate RAM

## Comparison with Similar Tools
- **GraphHopper** — Java-based, supports more profiles out of the box, easier customization
- **Valhalla** — multi-modal routing with transit support, dynamic costing, tiled data
- **Google Directions API** — cloud service, broader data coverage, usage-based pricing
- **pgRouting** — SQL-based routing inside PostgreSQL, slower but tightly integrated with spatial queries

## FAQ
**Q: How much RAM does OSRM need?**
A: A planet-wide dataset requires roughly 64 GB RAM. Country-level extracts typically need 2-8 GB.

**Q: Can OSRM handle real-time traffic?**
A: OSRM uses static edge weights. For live traffic, you need to reprocess data or use tools that support dynamic costing.

**Q: Does it support public transit routing?**
A: No. OSRM focuses on road-network routing. For multi-modal transit, consider Valhalla or OpenTripPlanner.

**Q: How often should I update the map data?**
A: Reprocess from a fresh OSM extract weekly or monthly depending on how current your routes need to be.

## Sources
- https://github.com/Project-OSRM/osrm-backend
- http://project-osrm.org/

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Source: https://tokrepo.com/en/workflows/asset-30d7ed39
Author: AI Open Source