1. School of Computers, Guangdong University of Technology, Guangzhou Guangdong 510006, China; 2. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Abstract：Concerning poor matching effect of existing map matching algorithms (such as classical Hidden Markov and its variants, advanced algorithms) for low-frequency trajectory data, a trajectory data mining method based on massive bus historical trajectory data was proposed. Taking bus stations as the sequence skeleton firstly, by mining, extracting, regrouping and sorting trajectory data from a large number of low frequency trajectory points to form high frequency trajectory data, then the high-frequency trajectory data sequence was processed by the map matching algorithm based on classical hidden Markov model to get the bus route map matching results. Compared with the matching method on the low-frequency data not processed by the mining algorithm, the proposed method reduces the matching error by an average of 6.3%, requires smaller data size and costs less time. In addition, this method is robust to low-frequency, unstable noise trajectory data, and it is suitable for map matching of all bus routes.
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2018, Vol. 38 
