Deep learning enabled vehicle trajectory map-matching method with advanced spatial

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Deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis

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Liu, Zhijia / Fang, Jie / Tong, Yingfang / Xu, Mengyun

Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial–temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.