摘要
针对城市中可能同时发生多起事故的严峻形势,研究拥堵交通状态下的应急救援问题,构建了一种疏散与调度协同决策的双层优化模型。上层模型考虑行程距离得到备选救援路径;下层模型以救援最公平和行程时间最小为目标,以救援需求和时间窗为约束优化应急车辆调度方案,以路段最大流量和事故严重程度为约束优化交通疏散方案。为求解下层协同决策模型,设计了一种具有基因表达调控策略编码的改进遗传算法。算例结果表明:沿救援路径进行交通疏散,救援总行程时间较疏散前降低了24.02%;与BA、SFLA两种群智能优化算法相比,改进遗传算法最优解的平均目标值分别减少了10.17%、13.19%。
Aiming at the severe situation that multiple accidents may occur simultaneously in the city,a two-layer optimization model for collaborative decision making of evacuation and dispatch was constructed to study the emergency rescue problem under congested traffic conditions.In the upper-level model,the travel distance was considered to obtain alternative rescue paths;in the lower-level model,the most equitable rescue and the minimum travel time was taken as the objectives,and the emergency vehicle dispatching scheme with the constraints of rescue demand and time window was optimized,and the traffic evacuation scheme with the constraints of maximum traffic flow and accident severity on the roadway.An improved genetic algorithm with encoding of gene expression regulation strategy was designed for solving the lower-level collaborative decision model.The algorithm results showed that the total rescue travel time is reduced by 24.02%compared to the pre-evacuation time by evacuating the traffic along the rescue path.Compared with the other two swarm intelligence optimization algorithms of BA and SFLA,the average objective value of the optimal solution of the improved genetic algorithm is respectively reduced by 10.17%and 13.19%.
作者
李雅倩
陈西江
班亚
韩贤权
杨嘉乐
LI Yaqian;CHEN Xijiang;BAN Ya;HAN Xianquan;YANG Jiale(School of Safety Science and Emergency Management,Wuhan University of Technology,Wuhan 430070,China;不详)
出处
《武汉理工大学学报（信息与管理工程版）》
CAS
2024年第2期188-194,共7页
Journal of Wuhan University of Technology：Information & Management Engineering
基金
湖北省自然科学基金项目(2023AFB950).
关键词
应急车辆
调度决策
遗传算法
交通疏散
双层规划
emergency vehicles
scheduling decision
genetic algorithm
traffic evacuation
bi-level programming
