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A Fast Real-time Map-Matching for Unstable Sampling-rate GPS Trajectories

Map matching is the process of matching GPS points to roads. Local methods and incremental methods usually have faster-running speed, however, the performance of these methods may be insufficient for complex road networks and data with significant error. Global methods can get better results in complex situations, while most of them handle high-frequency GPS data slowly. In order to solve these problems, a weight-based algorithm(FWMM) is developed in this paper. We introduce a new method of weight fusion to avoid the influence of dimension and adopt Particle Swarm Optimization(PSO) for parameter estimation. In order to accelerate the matching process of data with a high sampling rate, we designed a mechanism which can adaptively accelerate high-frequency data piece. Thus this method can be more accurate than existing methods mentioned above.