China Safety Science Journal ›› 2025, Vol. 35 ›› Issue (12): 213-220.doi: 10.16265/j.cnki.issn1003-3033.2025.12.1472

• Public safety • Previous Articles     Next Articles

Resilience analysis of urban road networks based on LSTM model for rainfall-induced day-to-day traffic flow degradation

WANG Hongwei(), RUAN Xiaobo**(), LI Yulong, TANG Yutao, DING Jianxun   

  1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei Anhui 230009, China
  • Received:2025-07-10 Revised:2025-10-11 Online:2025-12-27 Published:2026-06-28
  • Contact: RUAN Xiaobo

Abstract:

To explore the time-varying performance of urban road networks under regular rainfall and establish a reasonable evaluation system for road network resilience, an LSTM model for the rainfall-induced traffic flow degradation in urban road segments was first constructed, and a network performance function with time-varying characteristics was second defined. Then, based on the resilience concept, a time-varying resilience calculation model for road networks was derived. Finally, the effect of rainfall on the time-varying characteristics of road segments and networks was investigated based on rainfall information and traffic data of a certain city and the Sioux Falls network. The results show that when the duration or amount of rainfall increases, the traffic flow degradation of road segments increases on weekdays. The road network resilience is a comprehensive reflection of the synergistic effect of various related road segments, and therefore its response to rainfall is slower than that of traffic flow degradation, and its value is generally not less than 0.9 under regular rainfall conditions. The road network resilience during low rainfall seasons is significantly stronger than that during high rainfall seasons, and the annual resilience of road networks is usually maintained at a higher level when not affected by rainfall disasters, and the difference between adjacent years is not significant.

Key words: day-to-day traffic flow, rainfall-induced degradation, long short-term memory (LSTM), urban road networks, road network resilience

CLC Number: