考虑路段恢复差异的道路网络恢复决策优化

doi:10.16183/j.cnki.jsjtu.2023.031

摘要/Abstract

摘要：

现有路网恢复决策研究忽略了不同路段恢复速度和恢复程度差异对于路网恢复效果的影响.针对该问题,首先构建基于路段阻抗容忍度的路网连通性指标,以开展路段通行能力部分恢复情况下的路网性能评价;然后,以加权路网性能韧性和恢复速度韧性为优化目标,构建应急恢复决策双层优化模型,在确定待恢复路段的最优集合及恢复时序的同时,通过路段层面的资源分配与预算分配获得待恢复路段的恢复程度和恢复速度;最后,基于传统并行机调度问题遗传算法,构建新型编译码方法求解上层模型,基于Frank-Wolfe算法求解下层模型.基于贵州省区域高速公路网数据,对上述模型和算法进行了验证和分析.结果表明:在一定的资源和预算约束下,考虑路段恢复程度差异可提高32.62%的路网性能韧性,考虑路段恢复速度差异可提高10.17%的路网性能韧性.敏感性分析表明:考虑路段恢复速度差异可以提高增加恢复资源数量对于路网性能韧性、恢复速度韧性和加权韧性提升的边际效益,分别为12.69%、5.47%和22.93%.考虑路段恢复程度差异有助于平衡恢复预算增加导致的路网性能韧性的提高和恢复速度韧性的降低,保障路网恢复效果.因此考虑不同路段恢复差异对于路网恢复决策制定有重要意义.

关键词: 道路交通网络, 恢复决策优化, 双层规划模型, 网络韧性, 遗传算法

Abstract:

Existing studies on road network recovery decision have ignored the impact of the differences in recovery speed and recovery degree of different road sections on the recovery performance of the road network. To address this problem, road network connectivity index based on section impedance tolerance was first constructed to evaluate road network performance under partial recovery of road section capacity. Then, a bi-level optimization model for emergency recovery decisions was constructed with the weighted road network performance resilience and recovery speed resilience as optimization objectives. When the optimal set and recovery time sequence of the road sections to be repaired are determined, the recovery degree and speed of the road sections to be restored are obtained through resource allocation and budget allocation at the road section level. Finally, based on the traditional parallel machine scheduling problem genetic algorithm, a new encoding and decoding method was constructed to solve the upper model. The lower level model was solved based on the Frank-Wolfe algorithm. Based on the data of a regional expressway network in Guizhou Province, the above models and algorithms were verified and analyzed. The results show that under certain resource and budget constraints, considering the difference in road section recovery degree can improve the road network performance resilience by 32.62%. Considering the difference in road section recovery speed can improve the road network performance resilience by 10.17%. The sensitivity analysis shows that taking into consideration the difference in road section recovery speed can improve the marginal benefits of increasing the number of recovery resources for improving road network performance resilience, recovery speed resilience, and weighted resilience by 12.69%, 5.47%, and 22.93% respectively. Considering the difference in road section recovery degree helps balance the improvement of road network performance resilience and the reduction of recovery speed resilience caused by the increase of recovery budget, so as to ensure the road network recovery performance. Therefore, it is important to consider the recovery differences in different road sections for road network recovery decision.

Key words: road traffic network, recovery decision optimization, bi-level programming model, network resilience, genetic algorithm

中图分类号:

U113

路庆昌, 刘鹏, 秦汉, 徐鹏程. 考虑路段恢复差异的道路网络恢复决策优化[J]. 上海交通大学学报, 2024, 58(7): 1118-1129.

LU Qingchang, LIU Peng, QIN Han, XU Pengcheng. Optimization of Road Network Recovery Decisions Considering Road Section Recovery Differences[J]. Journal of Shanghai Jiao Tong University, 2024, 58(7): 1118-1129.

图/表 16

图1

图2

图3

表1

受损路段参数

a	$C a, t 0$ / (辆·h^-1)	v_a/ %	d_a/ km	a	$C a, t 0$ / (辆·h^-1)	v_a/ %	d_a/ km
3	4 200	50	54	11	4 200	75	72
4	4 200	100	125	12	4 200	75	77
5	4 200	50	35	13	4 200	100	57
6	4 200	50	30	14	4 200	50	71
7	4 200	100	54	15	4 200	100	86
8	4 200	75	49	16	4 200	50	61
9	4 200	25	64	17	4 200	100	27
10	4 200	50	74	-	-	-	-

表1

表2

图4

图5

图6

表3

图7

表4

图8

图9

图10

图11

图12

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l	ζ_l	σ_l/(km·辆·h^-1·d^-1)	m	ρ_m/%
0	4	1 200	0	100
1	3	900	1	75
2	2	600	2	50
3	1	300	3	25

决策	R	R₁	R₂	R₃
最优决策	0.57	0.65	0.47	59 496
决策1	0.49	0.49	0.48	58 490
决策2	0.53	0.59	0.47	59 496
决策3	0.54	0.64	0.44	59 496

n	λ_n	n	λ_n
1	0.004 952	11	0.019 446
2	0.042 081	12	0.049 604
3	0.098 184	13	0.042 434
4	0.051 280	14	0.052 372
5	0.056 473	15	0.074 173
6	0.033 682	16	0.018 612
7	0.076 761	17	0.030 656
8	0.057 031	18	0.070 485
9	0.067 571	19	0.050 111
10	0.063 737	20	0.040 354