A Novel Method to Evaluation Node Importance in Multilayer Regional Rail Transit Network

Ju, Yanni; Li, Zongping; Chen, Yufan; Feng, Runchao

doi:10.1007/978-981-19-2259-6_26

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the shortcomings of the above studies, some scholars have attempted to use the exhaustive method to explore the restoration time sequences after multisite failures [7,8].…”

Section: Introductionmentioning

confidence: 99%

Temporal Study of Subway Network Recovery under Resilience Perspective

Xu,

Lin,

Jiang

2024

Preprint

View full text Add to dashboard Cite

In order to improve the safety and emergency response capability of urban rail transit network, a resilience maximization-based urban rail transit network recovery model is designed. The shortcomings of the Space-L method in network model construction are compensated by introducing virtual transfer edges to capture the transfer relationships between lines. The travel decisions of passengers under four failure scenarios are considered to ensure that the travel behaviors of individuals in the network are more realistic. By calculating the time of passengers arriving at the failed stations and generating the real-time network, the affected population is more accurately identified, and the accuracy of the network passenger flow allocation is improved. Starting from the optimization of station restoration timing, with the goal of minimizing the cumulative performance loss during the whole process of emergencies, the resilience maximization restoration model is constructed, and the optimal restoration strategy under different failure scenarios is solved by combining the genetic algorithm with the optimized particle swarm GA-PSO algorithm. The experimental results based on the AFC data of Beijing subway network show that the recovery effect of the toughness maximization-based recovery strategy is better than that of the random recovery strategy and the empirical recovery strategy in all the four failure scenarios. Meanwhile, the performance loss during network recovery can be reduced and the benefits can be maximized by increasing the repair team.

show abstract

“…To overcome the shortcomings of the above studies, some scholars have attempted to use the exhaustive method to explore the restoration time sequences after multisite failures [7,8].…”

Section: Introductionmentioning

confidence: 99%