A Self-adjusting Online Map Matching Method

Abstract

Abstract With the rapid development of intelligent transportation systems (ITS) and location-based services (LBS), it is more important to match the original trajectory sequence generated by users/vehicles to the actual road network. Most of the existing online map matching algorithms are based on the idea of local processing, or require richer data input and more mathematical models to ensure matching accuracy. This paper presents a simple and effective map matching method, called self-adjusting online map matching (AOMM). The algorithm is developed based on hidden Markov model (HMM). Considering the topological and geometric properties of the road network, the emission probability and transition probability calculation formulas of HMM are defined. And three adjustment strategies are provided to deal with trajectory noise points, dense trajectory points, and offset trajectory points. The algorithm only needs latitude and longitude information of trajectory points, and can match point by point. Experimental results on open trajectory data show that the algorithm has high matching accuracy and low output delay, and can meet the requirements of general online map matching tasks.