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Hidden Markov map matching based on trajectory segmentation with heading homogeneity

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Abstract

Map matching is to locate GPS trajectories onto the road networks, which is an important preprocessing step for many applications based on GPS trajectories. Currently, hidden Markov model is one of the most widely used methods for map matching. However, both effectiveness and efficiency of conventional map matching methods based on hidden Markov model will decline in the dense road network, as the number of candidate road segments enormously increases around GPS point. To overcome the deficiency, this paper proposes a segment-based hidden Markov model for map matching. The proposed method first partitions GPS trajectory into several GPS sub-trajectories based on the heading homogeneity and length constraint; next, the candidate road segment sequences are searched out for each GPS sub-trajectory; last, GPS sub-trajectories and road segment sequences are matched in hidden Markov model, and the road segment sequences with the maximum probability is identified. A case study is conducted on a real GPS trajectory dataset, and the experiment result shows that the proposed method improves the effectiveness and efficiency of the conventional HMM map matching method.

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Authors and Affiliations

  1. Department of Geomatics Engineering, University of Calgary, 2500 University Dr. N.W, Calgary, Alberta, T2N 1N4, Canada

    Ge Cui & Xin Wang

  2. School of Information and Technology, Northwest Univeristy, 229 North Taibai Road, 710069, Xi’an, Shaanxi, China

    Wentao Bian & Xin Wang

Authors
  1. Ge Cui
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  2. Wentao Bian
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  3. Xin Wang
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Correspondence to Xin Wang.

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Cui, G., Bian, W. & Wang, X. Hidden Markov map matching based on trajectory segmentation with heading homogeneity. Geoinformatica 25, 179–206 (2021). https://doi.org/10.1007/s10707-020-00429-4

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  • DOI: https://doi.org/10.1007/s10707-020-00429-4

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