吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (9): 2969-2977.doi: 10.13229/j.cnki.jdxbgxb.20240473

• 交通运输工程·土木工程 • 上一篇    

积水条件下城市路网性能恢复决策优化

贾洪飞1(),庄博1,罗清玉1(),刘玲2,黄秋阳1   

  1. 1.吉林大学 交通学院，长春 130022
    2.长春建筑学院 土木工程学院，长春 130607
  • 收稿日期:2024-04-30 出版日期:2025-09-01 发布日期:2025-11-14
  • 通讯作者: 罗清玉 E-mail:jiahf@jlu.edu.cn;luoqy@jlu.edu.cn
  • 作者简介:贾洪飞（1969-），男，教授，博士.研究方向：交通网络分析.E-mail：jiahf@jlu.edu.cn
  • 基金资助:
    吉林省科技发展计划项目(20230203127SF);国家自然科学基金项目(52302434)

Decision optimization of urban road network performance restoration under waterlogged conditions

Hong-fei JIA1(),Bo ZHUANG1,Qing-yu LUO1(),Ling LIU2,Qiu-yang HUANG1   

  1. 1.College of Transportation，Jilin University，Changchun 130022，China
    2.College of Civil Engineering，Changchun University of Architecture and Civil Engineering，Changchun 130607，China
  • Received:2024-04-30 Online:2025-09-01 Published:2025-11-14
  • Contact: Qing-yu LUO E-mail:jiahf@jlu.edu.cn;luoqy@jlu.edu.cn

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摘要：

为探究积水条件下最优的路网恢复决策方案，考虑了积水深度的动态变化，构建了城市三车道路段元胞自动机仿真模型。通过改进车辆换道与位置更新规则，描述积水条件下的驾驶行为变化，并仿真积水对路段通行能力的影响；分析积水导致车辆在部分路段通行受限时交通流在路网上的分布状态变化，建立以累积路网性能指标最大化为目标的恢复决策优化模型，并采用遗传算法与Frank-Wolfe算法相结合的方式求解。选取Sioux-Falls网络对模型进行验证，结果表明：求解模型得到的恢复策略对路网性能的恢复效果优于传统基于出度和基于流量的经验恢复策略，提升程度分别为3.84%、2.02%；路段恢复顺序不仅要考虑路段的受损程度，还要考虑路网的交通需求分布；恢复资源的增加会显著缩短路网的恢复时间，但边际效用逐渐降低。

关键词: 交通运输规划与管理, 城市道路网络, 积水, 元胞自动机, 决策优化, 双层规划模型

Abstract:

To explore the optimal road network restoration decision scheme under waterlogged conditions， a urban three-lane road sections cellular automaton simulation model was constructed considering the dynamic change of waterlogged depth. By improving the vehicle lane change and position update rules， the changes of driving behavior under waterlogged conditions were described， and the impact of waterlogged on road sections traffic capacity was simulated. The changes in the distribution of traffic flow on the road network caused by water accumulation leading to restricted passage of vehicles on certain road sections was analyzed， a recovery decision optimization model aiming at maximizing the cumulative road network performance index was established， and the genetic algorithm and Frank-Wolfe algorithm were combined to solve the problem. Sioux-Falls network was selected to verify the model，the results show that the recovery strategy obtained by the provided model has better effect on road network performance restoration than the empirical recovery strategies based on output and flow， and the improvement degree is 3.84% and 2.02%， respectively. The road section restoration order should not only consider the damage degree of the road section， but also consider the traffic demand distribution of the road network. The increase of recovery resources will significantly reduce the recovery time of the road network， but the marginal utility will gradually decrease.

Key words: transportation planning and management, urban road network, waterlogged, cellular automaton, decision optimization, bi-level programming model

中图分类号: 

  • U491

图1

路段区域划分及参数说明"

图2

车辆强制换道示意图"

图3

积水条件下路网性能的变化曲线"

图4

双层规划模型求解算法流程"

图5

Sioux-Falls路网结构"

表1

各车道组的车道数与受积水影响的车道数 (条)"

参数车道组编号
313171819323760
车道数32231233
受积水影响车道数21111233

图6

迭代曲线图"

图7

不同积水条件下积水影响系数的变化"

图8

目标恢复策略受损路段恢复顺序"

图9

各时刻路网性能变化曲线"

图10

累积路网性能变化曲线"

图11

不同恢复资源下各时刻路网性能变化曲线"

图12

不同恢复资源下累积路网性能变化曲线"

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