Map matching

The most common approach is to take recorded, serial location points (e.g. from GPS) and relate them to edges in an existing street graph (network), usually in a sorted list representing the travel of a user or vehicle.

Matching observations to a logical model in this way has applications in satellites navigation, GPS tracking of freight, and transportation engineering.

[2] Uses for map-matching algorithms range from the immediate and practical, such as applications designed for guiding travellers, to the analytical, such as generating detailed inputs for traffic analysis models and the like.

It accounts for the structure of the network, path constraints, and the sequence of GPS points to provide accurate and realistic route matching, especially in complex environments.

Advanced map-matching algorithms, including those based on Fuzzy Logic, Hidden Markov Models (HMM), and Kalman filters, significantly enhance the accuracy of GPS point location estimation.