An Online Map Matching Algorithm for Path-Free Trajectories by Integrating Path-Constrained Trajectories

• Published in: IEEE transactions on intelligent transportation systems Vol. 26; no. 8; pp. 12661 - 12675
• Main Authors: Li, Kaijie, Zhao, Juanjuan, Song, Yuchen, Liu, Xiaoying, Gao, Xitong, Ye, Kejiang, Xu, Chengzhong
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Accuracy; Global Positioning System; GPS error distribution; GPS trajectories; Hidden Markov models; Location awareness; map matching; Public transportation; Real-time systems; Roads; Trajectory; Urban areas; urban traffic; Vehicle dynamics
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2025.3555568

Online map matching (MM) aligns real-time GPS trajectories with digital road networks, playing a vital role in vehicle navigation, route planning, and traffic analysis. Hidden Markov Models (HMMs) are widely used for their interpretability and ability to handle low GPS sampling rates. However, in urban scenarios characterized by complex road networks, significant GPS localization error, and dynamic traffic conditions, existing HMM-based methods face challenges such as large road search spaces due to uniform GPS localization error distributions (GLED) and inaccurate route accessibility estimates stemming from inadequate consideration of real-time traffic conditions. This paper proposes an improved HMM-based MM method, recognizing that urban vehicle trajectories can be categorized into two types: path-free (e.g., taxis, private cars) and path-constrained (e.g., buses). Analyzing path-constrained trajectories helps estimate fine-grained GLED and real-time traffic states of path-free vehicles more precisely. The novelty of our approach lies in two aspects: i) Using a hierarchical spectral clustering algorithm based on GPS localization errors of path-constrained bus trajectories, a city is divided into fine-grained sub-regions with consistent GLED. This enables the HMM an adaptive road search scopes, improving online MM efficiency. ii) Gradient boosting trees, known for their interpretability, estimate free-flow speeds by integrating path-constrained trajectories with the factors like road attributes and time, optimizing HMM state transition probabilities for path-free trajectory MM. Experiments on real-world data demonstrate that the optimized HMM methods, leveraging different trajectory types, significantly enhance MM efficiency and accuracy compared to baseline models.The codebase of our methods and datasets are available at https://github.com/jacklee018/onlineMM-IPCT .