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Data-driven drone pre-positioning for traffic accident rapid assessment

Author

Listed:
  • Meng, Zhu
  • Zhu, Ning
  • Zhang, Guowei
  • Yang, Yuance
  • Liu, Zhaocai
  • Ke, Ginger Y.

Abstract

A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires rapid accident investigation and management. In this paper, we aim to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report. Methodology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic accidents within a scenario-wise Wasserstein ambiguity set. To the best of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with the CVaR metric. We demonstrate that the proposed RSO model can be reformulated into a mixed-integer programming model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark models. The numerical results yield valuable insights for traffic supervisory agencies.

Suggested Citation

  • Meng, Zhu & Zhu, Ning & Zhang, Guowei & Yang, Yuance & Liu, Zhaocai & Ke, Ginger Y., 2024. "Data-driven drone pre-positioning for traffic accident rapid assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:transe:v:183:y:2024:i:c:s1366554524000425
    DOI: 10.1016/j.tre.2024.103452
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    as
    1. Zhi Chen & Melvyn Sim & Peng Xiong, 2020. "Robust Stochastic Optimization Made Easy with RSOME," Management Science, INFORMS, vol. 66(8), pages 3329-3339, August.
    2. Outay, Fatma & Mengash, Hanan Abdullah & Adnan, Muhammad, 2020. "Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 116-129.
    3. Ahmadi-Javid, Amir & Fallah-Tafti, Malihe, 2019. "Portfolio optimization with entropic value-at-risk," European Journal of Operational Research, Elsevier, vol. 279(1), pages 225-241.
    4. Zhaowei Hao & Long He & Zhenyu Hu & Jun Jiang, 2020. "Robust Vehicle Pre‐Allocation with Uncertain Covariates," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 955-972, April.
    5. Nilay Noyan & Gábor Rudolf & Miguel Lejeune, 2022. "Distributionally Robust Optimization Under a Decision-Dependent Ambiguity Set with Applications to Machine Scheduling and Humanitarian Logistics," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 729-751, March.
    6. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.
    7. Zhang, Guowei & Jia, Ning & Zhu, Ning & He, Long & Adulyasak, Yossiri, 2023. "Humanitarian transportation network design via two-stage distributionally robust optimization," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    8. Li, Yuchen & Liu, Yang, 2023. "Distributionally robust optimization for collaborative emergency response network design," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 176(C).
    9. Ajay Bhaskarabhatla & Luis Cabral & Deepak Hegde & Thomas Peeters, 2021. "Are Inventors or Firms the Engines of Innovation?," Management Science, INFORMS, vol. 67(6), pages 3899-3920, June.
    10. John Gunnar Carlsson & Erick Delage, 2013. "Robust Partitioning for Stochastic Multivehicle Routing," Operations Research, INFORMS, vol. 61(3), pages 727-744, June.
    11. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    12. Yin, Yunqiang & Yang, Yongjian & Yu, Yugang & Wang, Dujuan & Cheng, T.C.E., 2023. "Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    13. Zheng, Wei & Li, Bo & Song, Dongping & Li, Yanran, 2023. "Innovative development strategy of a risk-averse firm considering product unreliability under competition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    14. Elçi, Özgün & Noyan, Nilay, 2018. "A chance-constrained two-stage stochastic programming model for humanitarian relief network design," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 55-83.
    15. Aharon Ben-Tal & Dick den Hertog & Anja De Waegenaere & Bertrand Melenberg & Gijs Rennen, 2013. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Management Science, INFORMS, vol. 59(2), pages 341-357, April.
    16. Espadaler-Clapés, Jasso & Barmpounakis, Emmanouil & Geroliminis, Nikolas, 2023. "Empirical investigation of lane usage, lane changing and lane choice phenomena in a multimodal urban arterial," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    17. Cihan Tugrul Cicek & Zuo-Jun Max Shen & Hakan Gultekin & Bulent Tavli, 2021. "3-D Dynamic UAV Base Station Location Problem," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 839-860, July.
    18. Robert Jacobson & Natalie Mizik, 2009. "The Financial Markets and Customer Satisfaction: Reexamining Possible Financial Market Mispricing of Customer Satisfaction," Marketing Science, INFORMS, vol. 28(5), pages 810-819, 09-10.
    19. Zhao, Yue & Chen, Zhi & Lim, Andrew & Zhang, Zhenzhen, 2022. "Vessel deployment with limited information: Distributionally robust chance constrained models," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 197-217.
    20. Chen, Qingxin & Fu, Chenyi & Zhu, Ning & Ma, Shoufeng & He, Qiao-Chu, 2023. "A target-based optimization model for bike-sharing systems: From the perspective of service efficiency and equity," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 235-260.
    21. Ouyang, Pengying & Liu, Pan & Guo, Yanyong & Chen, Kequan, 2023. "Effects of configuration elements and traffic flow conditions on Lane-Changing rates at the weaving segments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    22. Angelica Gianfreda & Derek Bunn, 2018. "A Stochastic Latent Moment Model for Electricity Price Formation," BEMPS - Bozen Economics & Management Paper Series BEMPS46, Faculty of Economics and Management at the Free University of Bozen.
    23. Li, Runjie & Cui, Zheng & Kuo, Yong-Hong & Zhang, Lianmin, 2023. "Scenario-based Distributionally Robust Optimization for the Stochastic Inventory Routing Problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 176(C).
    24. Cao, Yunzhi & Zhu, Xiaoyan & Yan, Houmin, 2022. "Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    25. Bigazzi, Alexander Y. & Figliozzi, Miguel A., 2013. "Marginal costs of freeway traffic congestion with on-road pollution exposure externality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 12-24.
    26. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    27. Wang, Weiqiao & Yang, Kai & Yang, Lixing & Gao, Ziyou, 2021. "Two-stage distributionally robust programming based on worst-case mean-CVaR criterion and application to disaster relief management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    28. Gohram Baloch & Fatma Gzara, 2020. "Strategic Network Design for Parcel Delivery with Drones Under Competition," Transportation Science, INFORMS, vol. 54(1), pages 204-228, January.
    29. Zhu, Ning & Fu, Chenyi & Ma, Shoufeng, 2018. "Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 91-120.
    30. Ghaffarinasab, Nader & Çavuş, Özlem & Kara, Bahar Y., 2023. "A mean-CVaR approach to the risk-averse single allocation hub location problem with flow-dependent economies of scale," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 32-53.
    31. Tao, Liangyan & Liu, Sifeng & Xie, Naiming & Javed, Saad Ahmed, 2021. "Optimal position of supply chain delivery window with risk-averse suppliers: A CVaR optimization approach," International Journal of Production Economics, Elsevier, vol. 232(C).
    32. Meng, Shanshan & Guo, Xiuping & Li, Dong & Liu, Guoquan, 2023. "The multi-visit drone routing problem for pickup and delivery services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
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