Offline hidden markov map matching algorithm
Budget: $100 – $500 USD
Matching a trajectory with a network, e.g. a road network, is a classical task in geoinformatics. It can be differentiated into online, i.e. during the measurement of the trajectory like in car navigation, and offline, i.e. after the measurement of the trajectory, map matching. This plugin addresses only the offline map matching.
Because of inaccuracies of the GNSS signal and/or a low data quality of the network, the positions of the trajectory and the network are differing. In many cases a simple snapping of the trajectory on the network will not work. Reasons for that can be e.g. outliers in the trajectory or crossings of edges in the network.
This plugin provides a statistical approach to solve the problem of offline map matching using the principles of Hidden Markov Models (HMM) and the Viterbi algorithm.
Because of inaccuracies of the GNSS signal and/or a low data quality of the network, the positions of the trajectory and the network are differing. In many cases a simple snapping of the trajectory on the network will not work. Reasons for that can be e.g. outliers in the trajectory or crossings of edges in the network.
This plugin provides a statistical approach to solve the problem of offline map matching using the principles of Hidden Markov Models (HMM) and the Viterbi algorithm.