Map Matching Based on Seq2Seq with Topology Information

Abstract

Most existing road network matching algorithms are designed based on previous rules and do not fully utilize the potential of big data and historical tracks. To solve this problem, we introduce a new road network matching algorithm based on deep learning and using the topology information of the road network. Taking inspiration from the sequence-to-sequence (seq2seq) model popular in natural language processing, our algorithm builds multiple grid-dependent dictionaries based on the topology of road networks. Then the Byte Pair Encoding (BPE) algorithm is used to compress the grid dictionary, which effectively restricts the output range. A Bidirectional gated loop unit (Bi-GRU) with attention mechanisms is used as a recurrent neural network to capture information from a sequence of trajectory points. The model output feedback obtained by training the road network on Yibin City and the empirical evidence of the comparison in this experiment prove the effectiveness of the algorithm. When juxtaposed with similar algorithms, it shows superior accuracy and faster training speeds in road networks matching different scenarios.