Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China

Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delive...

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Main Authors: Shikun Xie, Zhen Yang, Mingxuan Wang, Guilong Xu, Shuming Bai
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/5/2688
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author Shikun Xie
Zhen Yang
Mingxuan Wang
Guilong Xu
Shuming Bai
author_facet Shikun Xie
Zhen Yang
Mingxuan Wang
Guilong Xu
Shuming Bai
author_sort Shikun Xie
collection DOAJ
description Sparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas.
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spelling doaj-art-e44f9956031a478ea3d07315e6321a272025-08-20T02:52:35ZengMDPI AGApplied Sciences2076-34172025-03-01155268810.3390/app15052688Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, ChinaShikun Xie0Zhen Yang1Mingxuan Wang2Guilong Xu3Shuming Bai4The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaSparse road networks in high-risk geological disaster areas, characterized by long segments, few nodes, and limited alternative routes, face significant vulnerabilities to geological hazards such as landslides, rockfalls, and collapses. These disruptions hinder emergency response and resource delivery, highlighting the need for enhanced resilience strategies. This study develops a dynamic resilience assessment framework using a two-layer topological model to analyze and optimize the resilience of such networks. The model incorporates trunk and local layers to capture dynamic changes during disasters, and it is validated using the road network in Tibet. The findings demonstrate that critical nodes, including tunnels, bridges, and interchanges, play a decisive role in maintaining network performance. Resilience is influenced by disaster type, duration, and traffic capacity, with collapse events showing moderate resilience and debris flows exhibiting rapid recovery but low survivability. Notably, half-width traffic interruptions achieve the highest overall resilience (0.7294), emphasizing the importance of partial traffic restoration. This study concludes that protecting critical nodes, optimizing resource allocation, and implementing adaptive management strategies are essential for mitigating disaster impacts and enhancing recovery. The proposed framework offers a practical tool for decision-makers to improve transportation resilience in high-risk geological disaster areas.https://www.mdpi.com/2076-3417/15/5/2688sparse road networksdynamic resilience assessmenttwo-layer topological modelgeological disastersemergency response
spellingShingle Shikun Xie
Zhen Yang
Mingxuan Wang
Guilong Xu
Shuming Bai
Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
Applied Sciences
sparse road networks
dynamic resilience assessment
two-layer topological model
geological disasters
emergency response
title Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
title_full Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
title_fullStr Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
title_full_unstemmed Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
title_short Evaluating the Resilience of Mountainous Sparse Road Networks in High-Risk Geological Disaster Areas: A Case Study in Tibet, China
title_sort evaluating the resilience of mountainous sparse road networks in high risk geological disaster areas a case study in tibet china
topic sparse road networks
dynamic resilience assessment
two-layer topological model
geological disasters
emergency response
url https://www.mdpi.com/2076-3417/15/5/2688
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