Abstract: As the complexity of urban transportation systems has significantly increased, existing map matching methods still face considerable challenges in handling complex urban traffic scenarios such as intersections and overpass obstructions. To address these issues, we propose a map-matching method tailored for complex urban road networks.Firstly, through the features of directionality and connectivity, we quantify the complexity of the road network scene where trajectory points reside and achieve trajectory segmentation. Then, for simple trajectories, we use a direction-constrained hidden Markov model (HMM) for matching, while for complex trajectory segments, a second-order model is adopted that uses the complexity of the road network as a weighting parameter to adaptively adjust the ratio of observation probabilities and transition probabilities in the HMM, improving the accuracy and efficiency of map matching in complex road networks.Finally, we compare traditional HMM methods with the ST-Matching method.The results show that the proposed algorithm improves matching accuracy by 5.4% and 6.0% respectively in complex scenarios and has higher matching efficiency.

Key words: road network complexity, HMM, map matching, adaptive algorithm