Utilizing OSRM for Efficient Location Data Preprocessing

In the modern era of technology, location data plays a crucial role in a vast array of applications and services. Nevertheless, real-world location data can be unreliable, imprecise, or even deceptive due to various factors. As an engineer working to enhance location-based services for an urban mobility provider in Mumbai, I have encountered various challenges caused by inaccurate GPS or OBD device locations. This blog post explores the potential of the Open Source Routing Machine (OSRM) in improving the accuracy of location data.

Understanding Real World Challenges of Location Data

Let's dive into some real-life situations that highlight the gravity of dealing with inaccurate location data in a bustling city like Mumbai.

Signal Confusion: Imagine a scenario where a bus is navigating through the busy streets of Mumbai, surrounded by tall buildings. Sometimes, the signals from GPS devices can bounce off these structures, resulting in incorrect location readings. This might make the bus appear to be on a different route or even off the road altogether. Passengers might find themselves perplexed about the bus's actual location.

Complex Crossroads: Visualize a bustling intersection like Bandra Worli Sea Link, where multiple lanes and ramps converge. If GPS inaccuracies misplace a bus even slightly off its intended route, the navigation system could misinterpret it as taking an unintended exit or lane change. Such confusion can lead to inaccurate route suggestions, delays, and even missed stops.

Lane-Based Confusion: Consider Mumbai's Western Express Highway, a road with lanes dedicated to different destinations. GPS inaccuracies might place a bus in the wrong lane, leading to incorrect navigation suggestions. The system might recommend wrong exits or turns, steering the bus onto an unintended path.

U-turn Uncertainty: Approaching a U-turn, accurate location data becomes crucial for estimating the time needed to complete the turn and rejoin the correct route. If GPS inaccuracies position the bus incorrectly before or after the U-turn, the estimated arrival time at subsequent stops could be way off. Passengers might receive misleading information, causing confusion and frustration.

These examples demonstrate the challenges that arise when dealing with location inaccuracies in Mumbai's intricate road network.

Location Preprocessing with OSRM

Relying solely on raw GPS coordinates can lead to location inaccuracies. This is where the Open Source Routing Machine (OSRM) comes into play. OSRM is a powerful open-source routing engine designed to process and enhance location data, making it more accurate and meaningful.

OSRM takes a unique approach to location data enhancement. It doesn't merely rely on individual GPS coordinates; instead, it considers the entire road network and intelligently matches incoming location data with the closest road segments. By doing so, OSRM can mitigate the effects of GPS inaccuracies, even when the reported location is slightly off the mark.

Installing and Deploying OSRM

Enhancing location accuracy using the Open Source Routing Machine (OSRM) begins with the installation and deployment of the OSRM server. The OSRM server processes location data and provides refined coordinates for improved accuracy. Here's a step-by-step guide to help you set up your OSRM environment.

Step 1: Get Geographical Data

Start by obtaining geographical data from OpenStreetMap (OSM), which includes road networks and intersections. Visit Geofabrik, select your region, and download the .osm data. For instance:

Example: wget http://download.geofabrik.de/asia/india/western-zone-latest.osm.pbf

Step 2: Run the OSRM server after preprocessing OSM data

To unlock OSRM's potential, preprocess OSM data using the OSRM Backend tool. The OSRM server can be easily run using Docker containers. Here's how:

Visit the OSRM GitHub repository for detailed instructions and tools.
Utilize Docker for easy setup. Follow the instructions at https://github.com/Project-OSRM/osrm-backend#using-docker.

In the upcoming section, we'll delve into the Nearest and Match Services, exploring how they optimize route calculations and refine location data.

API Endpoints: Nearest and Match Services

Once your OSRM server is up and running, you'll have access to powerful API endpoints from OSRM (http://project-osrm.org/docs/v5.5.1/api/#general-options) that can significantly enhance location accuracy. Two key services that play a pivotal role in this process are the Nearest and Match Services.

The Nearest Service is ideal for accurately mapping individual points to the nearest road segment, making it useful for imprecise GPS points. On the other hand, the Match Service is perfect for reconstructing vehicle routes from sequences of GPS points. It is optimized for maintaining route continuity, making it valuable for accurate navigation and travel time estimation.

To choose the right service, consider your specific application requirements. If you need accurate mapping without sequence consideration, go for the Nearest Service. If you need to reconstruct routes and ensure accurate navigation, opt for the Match Service.

In summary, the Nearest Service is for point mapping, while the Match Service excels in reconstructing routes and maintaining continuity for navigation accuracy.

For more in-depth information and practical implementation details, refer to the OSRM Nearest Service documentation and OSRM Match Service documentation.

It is very easy to get started with these services. In this blogpost, let's discuss the Match Service implementation in more details.

How the Match Service Works

The Match Service aligns a sequence of GPS points to the most likely route on the road network. It considers road geometry, direction, and nearby intersections to identify the optimal match. This process effectively "snaps" GPS points to their corresponding road segments, creating an accurate route.

Benefits of Using the Match Service

• Route Continuity: The Match Service maintains route continuity, ensuring accurate navigation instructions and travel time estimates.
• Navigation Precision: Snapped routes provide precise navigation guidance, reducing confusion and incorrect turns.
• Accurate Travel Time Estimates: Reliable route data leads to better travel time estimates, improving operational efficiency.

Implementation Example

To use the Match Service, send a sequence of GPS points to the OSRM server. Here's a simplified example in Python:

import requests

def match_gps_points(points):

    osrm_url = "http://your-osrm-server/match/v1/driving/" + ";".join([f"{point[1]},{point[0]}" for point in points])

    response = requests.get(osrm_url)

if response.status_code == 200:
    snapped_route = response.json()
    return snapped_route
else:
    return None



    

    





  
  
  Example GPS points


gps_points = [(19.076191, 72.875877), (19.072565, 72.874377), (19.071213, 72.869145)]

  
  
  Call the function


snapped_route = match_gps_points(gps_points)

print(snapped_route)

Conclusion

Using OSRM's Nearest and Match Services can easily help in preprocessing raw location data from GPS devices.