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Dynamic thresholds for the resilience assessment of road traffic networks to wildfires

Author

Listed:
  • Arango, Erica
  • Nogal, Maria
  • Yang, Ming
  • Sousa, Hélder S.
  • Stewart, Mark G.
  • Matos, José C.

Abstract

The severe effects of extreme wildfire events in recent years have shown that the fire suppression approach is not enough to solve the problem. An alternative to dealing with this issue is to accept the impossibility of eliminating wildfire hazards and focus on preparing systems to be more resilient. However, existing decision-making tools based on resilience present important drawbacks that make them inadequate for this task. This paper proposes a new approach and methodology for the resilience assessment of road traffic networks to wildfires that overcomes the main drawbacks, paying attention to the different functions of the system and the acceptance of a specific loss of performance. The latter is done through the introduction of dynamic thresholds that reflect the different requirements of the system under different wildfire conditions, including normal and extreme fires. The methodology is exemplified for five traffic networks. The results support the relevance of appropriate wildfire management through the adaptation of the natural and built environment to increase the capacity of the traffic networks to cope with wildfires.

Suggested Citation

  • Arango, Erica & Nogal, Maria & Yang, Ming & Sousa, Hélder S. & Stewart, Mark G. & Matos, José C., 2023. "Dynamic thresholds for the resilience assessment of road traffic networks to wildfires," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s0951832023003216
    DOI: 10.1016/j.ress.2023.109407
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    Cited by:

    1. Naseh Moghanlou, Lida & Di Maio, Francesco & Zio, Enrico, 2024. "Probabilistic scenario analysis of integrated road-power infrastructures with hybrid fleets of EVs and ICVs," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Zhou, Xinxin & Huang, Yun & Bai, Guanghan & Xu, Bei & Tao, Junyong, 2024. "The resilience evaluation of unmanned autonomous swarm with informed agents under partial failure," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    3. Liu, Qiang & Huang, Delong & Zhang, Bin & Tang, Aiping & Xu, Xiuchen, 2024. "Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Ricci, Federica & Misuri, Alessio & Scarponi, Giordano Emrys & Cozzani, Valerio & Demichela, Micaela, 2024. "Vulnerability Assessment of Industrial Sites to Interface Fires and Wildfires," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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