A high accuracy fuzzy logic based map matching algorithm for road transport

Recent research on map matching algorithms for land vehicle navigation has been based on either a conventional topological analysis or a probabilistic approach. The input to these algorithms normally comes from the global positioning system (GPS) and digital map data. Although the performance of some of these algorithms is good in relatively sparse road networks, they are not always reliable for complex roundabouts, merging or diverging sections of motorways, and complex urban road networks. In high road density areas where the average distance between roads is less than 100 m, there may be many road patterns matching the trajectory of the vehicle reported by the positioning system at any given moment. Consequently, it may be difficult to precisely identify the road on which the vehicle is travelling. Therefore, techniques for dealing with qualitative terms such as likeliness are essential for map matching algorithms to identify a correct link. Fuzzy logic is one technique that is an effective way to deal with qualitative terms, linguistic vagueness, and human intervention. This article develops a map matching algorithm based on fuzzy logic theory. The inputs to the proposed algorithm are from GPS augmented with data from deduced reckoning sensors to provide continuous navigation. The algorithm is tested on different road networks of varying complexity. The validation of this algorithm is carried out using high precision positioning data obtained from GPS carrier phase observables. The performance of the developed map matching algorithm is evaluated against the performance of several well-accepted existing map matching algorithms. The results show that the fuzzy logic-based map matching algorithm provides a significant improvement over existing map matching algorithms both in terms of identifying correct links and estimating the vehicle position on the links.