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An instant discovery method for companion vehicles based on incremental and parallel calculation

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  • Xu, Xinpeng
  • Tao, Hongfei
  • Wu, Weiguo
  • Liu, Song

Abstract

In the field of intelligent transportation, the instant discovery of companions has become a research hotspot. This technique can be applied to traffic management and public security governance. This study provides an incremental and distributed approach for discovering traveling companion instantly and continuously based on a data stream of automatic number plate recognition(ANPR). First, a parallelized incremental mining algorithm is designed and implemented in Spark on the basis of traffic-monitoring streaming data. Second, an adjustable data structure DF-tree is proposed that considers the characteristics of companion vehicles with the original ANPR data stream changing dynamically. On the basis of the DF-tree, the system can discover companions without reconstructing the data tree. In addition, we introduce a time decay mechanism to satisfy the spatio-temporal constraints of companion vehicles discovery. Finally, we realize the real-time discovery of companion vehicles based on large-scale ANPR data. The proposed methods are evaluated with extensive experiments on real datasets. The experimental results show that our proposed DF-tree-based approach is faster than the existing methods for companion discovery and it can detect companion vehicles groups in real time.

Suggested Citation

  • Xu, Xinpeng & Tao, Hongfei & Wu, Weiguo & Liu, Song, 2023. "An instant discovery method for companion vehicles based on incremental and parallel calculation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    • Handle: RePEc:eee:phsmap:v:610:y:2023:i:c:s0378437122009785
    DOI: 10.1016/j.physa.2022.128420
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    References listed on IDEAS

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    1. Yao, Zhihong & Zhao, Bin & Qin, Lingqiao & Jiang, Yangsheng & Ran, Bin & Peng, Bo, 2020. "An efficient heterogeneous platoon dispersion model for real-time traffic signal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Ruan, Tiancheng & Zhou, Linjie & Wang, Hao, 2021. "Stability of heterogeneous traffic considering impacts of platoon management with multiple time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    3. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
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