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<p>Traffic congestion is one of the most crucial factors that could affect emergency vehicle planning and endanger human lives in earthquake response. This paper aims to develop a mathematical formulation that utilizes a modified Bureau of Public Roads (BPR) function to effectively manage casualty transportation under real-world conditions, including traffic congestion. The deterministic optimization model is formulated as a multi-objective, multi-period, multi-modal transportation strategy for prioritized casualties. The mixed-integer non-linear programming (MNLP) model is linearized through a combination of separable and integer programming techniques. Then, a hybrid solution algorithm of lexicographic and weighted sum techniques is developed for solving the proposed linear formulation. Finally, an illustrative example is presented and analyzed to find the shortest paths in terms of travel time for emergency vehicle planning. Results indicated that traffic congestion significantly impacts travel time, emergency vehicle planning, and the assignment of injured individuals to relief vehicles and hospitals. The algorithm’s solution time was evaluated by creating test instances across various dimensions and planning periods and considering key parameters. Moreover, integrating the BPR function into urban transportation could improve casualty transportation during the earthquake response phase.</p>

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Optimizing injured people transportation in earthquake response: considering traffic congestion with BPR function

  • 期刊论文

  • 发表日期：

    2025年8月2日

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  • Sedigheh Khoushehchin Bahar,
  • Mehdi Najafi,
  • Seyyed Mohammad Hadji Molana

摘要

Traffic congestion is one of the most crucial factors that could affect emergency vehicle planning and endanger human lives in earthquake response. This paper aims to develop a mathematical formulation that utilizes a modified Bureau of Public Roads (BPR) function to effectively manage casualty transportation under real-world conditions, including traffic congestion. The deterministic optimization model is formulated as a multi-objective, multi-period, multi-modal transportation strategy for prioritized casualties. The mixed-integer non-linear programming (MNLP) model is linearized through a combination of separable and integer programming techniques. Then, a hybrid solution algorithm of lexicographic and weighted sum techniques is developed for solving the proposed linear formulation. Finally, an illustrative example is presented and analyzed to find the shortest paths in terms of travel time for emergency vehicle planning. Results indicated that traffic congestion significantly impacts travel time, emergency vehicle planning, and the assignment of injured individuals to relief vehicles and hospitals. The algorithm’s solution time was evaluated by creating test instances across various dimensions and planning periods and considering key parameters. Moreover, integrating the BPR function into urban transportation could improve casualty transportation during the earthquake response phase.