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Pre-siting of UAV stations for traffic accident assessment considering road dispersion

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  • Sizhe Wang
  • Yanying Shang

Abstract

This paper investigates the issue of pre-site selection for drone stations with the aim of enhancing the rapid assessment capability of urban road traffic accidents. Firstly, the influence of traffic accidents on urban traffic is analyzed, and the potential application of drones in rapid response at the accident scene is explored. A minimization model is constructed with the goal of minimizing the cost of accident handling and reducing traffic congestion. To solve this problem, we improved the simulated annealing algorithm by combining the multi-neighborhood strategy, adaptive neighborhood size, and adding a taboo list, and verified the effectiveness of the algorithm. The validity of the model is tested through simulation examples, and the impact of the drone coverage radius and the distribution of accident points on the model performance is explored through sensitivity analysis, providing management insights for the pre-site selection of drone stations.

Suggested Citation

  • Sizhe Wang & Yanying Shang, 2025. "Pre-siting of UAV stations for traffic accident assessment considering road dispersion," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-24, February.
    • Handle: RePEc:plo:pone00:0316431
    DOI: 10.1371/journal.pone.0316431
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    References listed on IDEAS

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    1. Dezhi Zhang & Xin Wang & Shuangyan Li & Nan Ni & Zhuo Zhang, 2018. "Joint optimization of green vehicle scheduling and routing problem with time-varying speeds," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-20, February.
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