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<p>Pluvial flooding disrupts road transport and emergency rescue operations, amplifying cascading risks across urban traffic systems and constraining disaster response effectiveness. Although traffic signal control can sustain partial flow and accelerate post-flood recovery, existing systems are insufficiently adaptive to rapidly changing inundation and traffic conditions, limiting their capacity to maintain critical traffic functions during extreme rainfall events. To address this issue, this study developed an adaptive signal control framework based on a coupled hydrodynamic-traffic-rescue multi-agent model to quantify how real-time signal strategies mitigate compound and cascading flood-traffic-rescue interactions and improve emergency response capacity under pluvial flood risk. Taking the Liangshui River Basin in Beijing as a case study, this study integrated empirical peak and off-peak traffic data with designed rainfall scenarios with return periods of 20, 50, and 100 years to evaluate two adaptive mechanisms: yellow-light phase control (YLPC), triggered by critical water depth, and green-light ratio optimization (GLRO), driven by real-time traffic feedback. The results reveal a strong spatial coupling between inundation corridors and congestion-prone traffic arteries, forming cascading flood-traffic risk pathways that amplify congestion propagation and emergency response delays. During flooding, maintaining baseline fixed-cycle signal operation under low-demand conditions shows stronger system resistance than premature YLPC activation, shortening emergency response time by up to 49.2 min, equivalent to a 12.6% reduction, while GLRO enhances combined rescue and traffic efficiency by 15.6% during peak hours. These findings indicate that adaptive signal control provides a transferable approach for interrupting flood-induced cascading impacts across traffic and emergency response systems under intensifying pluvial flooding.</p>

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Enhancing Urban Traffic Resilience Under Pluvial Flooding Through Adaptive Signal Control

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Naliang Guo,
  • Mengfei Zhang,
  • Yongkun Li,
  • Yali Zhang,
  • Feng Wu

摘要

Pluvial flooding disrupts road transport and emergency rescue operations, amplifying cascading risks across urban traffic systems and constraining disaster response effectiveness. Although traffic signal control can sustain partial flow and accelerate post-flood recovery, existing systems are insufficiently adaptive to rapidly changing inundation and traffic conditions, limiting their capacity to maintain critical traffic functions during extreme rainfall events. To address this issue, this study developed an adaptive signal control framework based on a coupled hydrodynamic-traffic-rescue multi-agent model to quantify how real-time signal strategies mitigate compound and cascading flood-traffic-rescue interactions and improve emergency response capacity under pluvial flood risk. Taking the Liangshui River Basin in Beijing as a case study, this study integrated empirical peak and off-peak traffic data with designed rainfall scenarios with return periods of 20, 50, and 100 years to evaluate two adaptive mechanisms: yellow-light phase control (YLPC), triggered by critical water depth, and green-light ratio optimization (GLRO), driven by real-time traffic feedback. The results reveal a strong spatial coupling between inundation corridors and congestion-prone traffic arteries, forming cascading flood-traffic risk pathways that amplify congestion propagation and emergency response delays. During flooding, maintaining baseline fixed-cycle signal operation under low-demand conditions shows stronger system resistance than premature YLPC activation, shortening emergency response time by up to 49.2 min, equivalent to a 12.6% reduction, while GLRO enhances combined rescue and traffic efficiency by 15.6% during peak hours. These findings indicate that adaptive signal control provides a transferable approach for interrupting flood-induced cascading impacts across traffic and emergency response systems under intensifying pluvial flooding.