Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (9): 2969-2977.doi: 10.13229/j.cnki.jdxbgxb.20240473

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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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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

CLC Number: 

  • U491

Fig.1

Road section area division and parameter description"

Fig.2

Schematic diagram of uehicle forced lane change"

Fig.3

Road network performance change curve"

Fig.4

Two-layer programming model solving algorithm flow"

Fig.5

Sioux-Falls road network structure"

Table 1

Number of lanes in lane group and number of lanes affected by waterlogging"

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

Fig.6

Iterative graph"

Fig.7

Change of water logged influence coefficient unber different water logged condition"

Fig.8

Target recovery strategy for recovery sequence of damaged sections"

Fig.9

Road network performance change curves at different"

Fig.10

Cumulative road network performance change curves"

Fig.11

Road network performance under change curves at different moments different recovery resources"

Fig.12

Cumulative road netword performance change curves under different recovery resources"

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