Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China
Extreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. Taki...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/735 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589188007985152 |
|---|---|
| author | Chenhui Liu Xue Su Zhichun Wu Yingjun Zhang Cuizhu Zhou Xiangguo Wu Yong Huang |
| author_facet | Chenhui Liu Xue Su Zhichun Wu Yingjun Zhang Cuizhu Zhou Xiangguo Wu Yong Huang |
| author_sort | Chenhui Liu |
| collection | DOAJ |
| description | Extreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. Taking the metro network of Chongqing, the largest mountainous city in China, as an example, this study establishes a network topology model to identify the high-risk nodes under rainfalls and find the effective recovery strategies. By introducing the metro ridership and topological shortest distances, a network service efficiency function is developed, and the importance of nodes is quantified using service efficiency index and topological importance index. The resilience assessment model based on service efficiency is constructed using the resilience triangle theory. Additionally, risk levels for landslide and flood-prone areas are classified using the K-means algorithm, based on rainfall, elevation, and slope data, identifying high-risk stations. Finally, the node recovery sequence and strategies for high-risk nodes affected by landslides and floods are examined. The results indicate that in extreme rainfall scenarios, two transfer stations (Daping and Fuhua Road) are among the high-risk landslide stations, while most other nodes have a service efficiency index of less than 0.2. High-risk flood stations are located on non-transfer lines and mostly on metro lines with high traffic flow, with service efficiency index generally high, with some stations, like Bijin Station, exceeding 0.3. When all affected nodes fail, network service efficiency decreases by 84.0% and 75.2% under landslide and flood disasters, respectively. Compared with the random recovery strategy, recovery strategies based on topological importance and service efficiency index, the optimal recovery strategy based on genetic algorithm performs much better. |
| format | Article |
| id | doaj-art-6b1e77cd454e4142a2ac4ee92c7293f3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6b1e77cd454e4142a2ac4ee92c7293f32025-01-24T13:20:38ZengMDPI AGApplied Sciences2076-34172025-01-0115273510.3390/app15020735Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, ChinaChenhui Liu0Xue Su1Zhichun Wu2Yingjun Zhang3Cuizhu Zhou4Xiangguo Wu5Yong Huang6College of Civil Engineering, Hunan University, Changsha 410082, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, ChinaHunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, ChinaHunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, ChinaChongqing Transport Planning Institute, Chongqing 401147, ChinaKey Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, ChinaExtreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. Taking the metro network of Chongqing, the largest mountainous city in China, as an example, this study establishes a network topology model to identify the high-risk nodes under rainfalls and find the effective recovery strategies. By introducing the metro ridership and topological shortest distances, a network service efficiency function is developed, and the importance of nodes is quantified using service efficiency index and topological importance index. The resilience assessment model based on service efficiency is constructed using the resilience triangle theory. Additionally, risk levels for landslide and flood-prone areas are classified using the K-means algorithm, based on rainfall, elevation, and slope data, identifying high-risk stations. Finally, the node recovery sequence and strategies for high-risk nodes affected by landslides and floods are examined. The results indicate that in extreme rainfall scenarios, two transfer stations (Daping and Fuhua Road) are among the high-risk landslide stations, while most other nodes have a service efficiency index of less than 0.2. High-risk flood stations are located on non-transfer lines and mostly on metro lines with high traffic flow, with service efficiency index generally high, with some stations, like Bijin Station, exceeding 0.3. When all affected nodes fail, network service efficiency decreases by 84.0% and 75.2% under landslide and flood disasters, respectively. Compared with the random recovery strategy, recovery strategies based on topological importance and service efficiency index, the optimal recovery strategy based on genetic algorithm performs much better.https://www.mdpi.com/2076-3417/15/2/735resiliencemountainous urban rail transitextreme rainfallshigh-risk node identificationgenetic algorithmresilience recovery strategy |
| spellingShingle | Chenhui Liu Xue Su Zhichun Wu Yingjun Zhang Cuizhu Zhou Xiangguo Wu Yong Huang Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China Applied Sciences resilience mountainous urban rail transit extreme rainfalls high-risk node identification genetic algorithm resilience recovery strategy |
| title | Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China |
| title_full | Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China |
| title_fullStr | Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China |
| title_full_unstemmed | Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China |
| title_short | Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China |
| title_sort | exploration of the mountainous urban rail transit resilience under extreme rainfalls a case study in chongqing china |
| topic | resilience mountainous urban rail transit extreme rainfalls high-risk node identification genetic algorithm resilience recovery strategy |
| url | https://www.mdpi.com/2076-3417/15/2/735 |
| work_keys_str_mv | AT chenhuiliu explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT xuesu explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT zhichunwu explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT yingjunzhang explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT cuizhuzhou explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT xiangguowu explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina AT yonghuang explorationofthemountainousurbanrailtransitresilienceunderextremerainfallsacasestudyinchongqingchina |