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Traffic trajectory data analysis technology based on HMM model map matching algorithm.

Mingkang Sun1, Xiang Li2

  • 1Glasgow College, University of Electronic Science and Technology of China, Chengdu, 610000, China.

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Summary
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This study introduces an improved hidden Markov model for map matching, enhancing traffic trajectory data accuracy. The algorithm achieves over 95% accuracy, outperforming traditional methods in complex road conditions.

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Area of Science:

  • Transportation Science
  • Data Analysis
  • Computer Science

Background:

  • Increasing volumes of traffic trajectory data necessitate advanced analysis techniques.
  • Current positioning data often deviates from real-world roads, and trajectory prediction models lack accuracy.
  • Existing map matching algorithms struggle with complex road geometries and data inaccuracies.

Purpose of the Study:

  • To develop a more accurate map matching algorithm for traffic trajectory data.
  • To address the limitations of existing models in handling positioning deviations and low prediction accuracy.
  • To improve the precision of processing and analyzing traffic trajectory data.

Main Methods:

  • A novel map matching algorithm based on hidden Markov models (HMMs) was developed.
  • The algorithm identifies candidate paths and points, then uses speed and angle changes to build a state transition matrix.
  • An optimal value of K=5 was determined for selecting nearest candidate paths, with processing time of 51 ms.

Main Results:

  • The proposed HMM-based algorithm achieved an overall accuracy of 95.3%, exceeding 96% in parallel and mixed road sections.
  • Accuracy rates under various road conditions were consistently higher than traditional HMM algorithms, reaching up to 98.3%.
  • The algorithm demonstrated stable and superior matching accuracy compared to existing methods, particularly in challenging road environments.

Conclusions:

  • The developed hidden Markov model-based map matching algorithm significantly enhances the accuracy of traffic trajectory data processing.
  • The method proves effective in diverse road conditions, offering a more precise alternative to traditional algorithms.
  • This research contributes to more reliable analysis of traffic trajectory data, benefiting transportation systems.