Research on parallelized real-time map matching algorithm for massive GPS data

In construction of smart city, numerous vehicles’ trajectory data are produced by Global Positioning System (GPS) to track their real time location. When these GPS data are processed by map matching, results can be used to support a large number of ITS applications such as real time road condition calculation, inspection of traffic event and emergency treatment. However, as the fast explosive growth of monitored vehicle number, massive GPS data proposes overwhelming challenges for map matching. Consequently, traditional map matching algorithms can hardly satisfy high demands for matching speed and accuracy. Therefore, a real time map matching algorithm for numerous GPS data is proposed to guarantee high matching accuracy and matching efficiency. Meanwhile, it can meet demands of GPS data processing required by the monitor of numerous vehicles within the city. Main contributions of the method are: (1) A Kalman filter based correcting algorithm is proposed to improve the matching accuracy of the traditional topological algorithm on the complicated road sections such as intersections and parallel roads. (2) Based on the Spark streaming framework, the serial map-matching algorithm is converted into a parallelized map-matching algorithm, which significantly improves the processing efficiency of the map matching. (3) A gridding method being applicable to the parallelized algorithm was proposed by the paper. The GPS data in the same grid were allocated to the same computing unit to improve the efficiency of the parallelized computation. Experimental results show that the matching accuracy of the algorithm demonstrated by the paper is increased by 10%; the matching efficiency is 25% higher than same amount of stand-alone computers. A cluster of 15 computers that operates the proposed algorithm is capable for the real time map matching for GPS data produced by 800 thousand vehicles, which can effectively and extensively support the lastingly increased demand for processing numerous GPS data.