Abstract: Map matching is one of the core technologies in intelligent transportation systems,aiming to map GPS trajectory data to urban road networks,eliminate positioning errors,and reconstruct actual driving paths.With the explosive growth of GPS tra-jectory data volume,traditional HMM(Hidden Markov Model)-based map matching methods struggle to meet real-time proces-sing requirements due to high computational costs and temporal dependency constraints.To address this,this paper proposes a fast map matching method based on a trajectory micro-segment model(Micro-Segment Fast Matching,MSFM).By leveraging a sliding window mechanism,the proposed method decomposes trajectories into fixed-length micro-segments and employs vecto-rized computation techniques in a distributed computing environment.This approach significantly improves computational efficiency while maintaining map matching accuracy.Experimental results demonstrate that,under a distributed cluster configuration,MSFM achieves a map matching speed of approximately 110 000 points per second,which is 7 times faster than baseline algorithms,while retaining a 95.86% matching accuracy.By optimizing the storage structure of trajectory data,the MSFM method exhibits significant performance advantages in real-time processing of large-scale trajectory data with high efficiency.

Key words: Map matching, Micro-segment, Hidden Markov model, Distributed computing, Vectorized computation