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Map Matching use cases, or why an Open Source and Open Data alternative is superior to the Google Maps Roads API

A map matching software makes your recorded GPS points ‘snap’ to some digital road network. E.g. the open source map matching component from GraphHopper is a highly customizable solution also available as SaaS. The following picture illustrates this map matching process from recorded data (blue) to matching data (green):

The screenshot shows the real world data in blue and the calculated matched roads in green.

This screenshot shows the real world data in blue and the calculated matching roads in green.

But why is this ‘snap to road’ useful? For that we will look today into three possible usage scenarios. Before we start we need to clarify the wording a bit. A GPS point is a location on earth with latitude and longitude data, and a GPX point is a GPS point with an associated timestamp. So when you record your walking or biking trail you often have a GPX file which is a list of GPX points.

1. Attach data available in the digital map

The Google Maps Roads API allows you to make GPX tracks align to their proprietary road network, which is useful. They also allow you to attach the maximum allowed speed but no further information and only for business customers. A solution like a map matching component from GraphHopper which is based on OpenStreetMap has some advantages over that:

  • You can attach not only maximum speed values to your data after the matching, but also turn instructions, street names, surface values, access restrictions, road types and well, nearly any way tag which is stored in OpenStreetMap. And as GraphHopper supports elevation, enhancing the input data with elevation afterwards is possible and easy too.
  • If you find a mistake in the matching which is caused due to a problem in the data or because of missing data in developing countries, then you can easily change this in OpenStreetMap and rerun again. For example allryder uses this process to track urban transport data in emerging regions. Also you can find illegal turns of bicyclists and more which e.g. urban planners can use to improve¬†bikeability of their city. You can read through a German description from ratrun of more of these data wrangling problems.
route_with_maxspeed

Attach any OpenStreetMap way tag to the GPX track like maxspeed=50 in this example.

2. Make recorded routes and speeds more precise

If you track vehicles the data will suffer from GPS device precision errors, which also means that the speed and location is not that exact. Having more precise road network data and use this to enhance your recorded data will help you to calculate more precise distance and speed values for your tracked vehicles.

3. Identical road segments

Once you have recorded the GPX tracks and transformed them into digital paths you know the road IDs. With that information you can easily detect if two vehicles are passing the same road although the GPS coordinates are completely different and just ‘similar’ in location. This makes it easier to assign values like the measured speed to a road. Also this data can be used for traffic influenced routing.

And more

Furthermore an open source solution makes it easy to

  1. Attach your own data like any user rating to the recorded data
  2. Customize the software to your needs e.g. for electric vehicles or improve the matching algorithm itself
  3. Combine the results with a powerful routing algorithm as done in the use case ‘Identical road segments’

Have fun when playing around and let us know about your use case!