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Vehicle trajectory reconstruction from automatic license plate reader data

Author

Listed:
  • Haiyang Yu
  • Shuai Yang
  • Zhihai Wu
  • Xiaolei Ma

Abstract

Using perception data to excavate vehicle travel information has been a popular area of study. In order to learn the vehicle travel characteristics in the city of Ruian, we developed a common methodology for structuring travelers’ complete information using the travel time threshold to recognize a single trip based on the automatic license plate reader data and built a trajectory reconstruction model integrated into the technique for order preference by similarity to an ideal solution and depth-first search to manage the vehicles’ incomplete records phenomenon. In order to increase the practicability of the model, we introduced two speed indicators associated with actual data and verified the model’s reliability through experiments. Our results show that the method would be affected by the number of missing records. The model and results of this work will allow us to further study vehicles’ commuting characteristics and explore hot trajectories.

Suggested Citation

  • Haiyang Yu & Shuai Yang & Zhihai Wu & Xiaolei Ma, 2018. "Vehicle trajectory reconstruction from automatic license plate reader data," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:2:p:1550147718755637
    DOI: 10.1177/1550147718755637
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    References listed on IDEAS

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    Cited by:

    1. Wang, Yinpu & An, Chengchuan & Ou, Jishun & Lu, Zhenbo & Xia, Jingxin, 2022. "A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Yixian Chen & Zhaocheng He, 2020. "Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    3. Wenhao Li & Chengkun Liu & Tao Wang & Yanjie Ji, 2024. "An innovative supervised learning structure for trajectory reconstruction of sparse LPR data," Transportation, Springer, vol. 51(1), pages 73-97, February.

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