Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model
As global urbanization accelerates, urban transportation systems increasingly face challenges from natural disasters and public safety issues. Resilience in urban transportation is essential for sustainable urban development. This study proposes an innovative, data-driven approach to quantify urban...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Environmental and Sustainability Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665972725001357 |
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| author | Zihao Guo Xuanling Zhou Cong Lu Shixin Li Yunlong Zhu Yinhang Liu Zhijian Li |
| author_facet | Zihao Guo Xuanling Zhou Cong Lu Shixin Li Yunlong Zhu Yinhang Liu Zhijian Li |
| author_sort | Zihao Guo |
| collection | DOAJ |
| description | As global urbanization accelerates, urban transportation systems increasingly face challenges from natural disasters and public safety issues. Resilience in urban transportation is essential for sustainable urban development. This study proposes an innovative, data-driven approach to quantify urban transportation resilience, integrating genetic algorithms (GA), backpropagation (BP) neural networks, and the entropy weighting method. This approach aims to eliminate the introduction of subjective biases by experts in traditional methods, providing a more accurate and objective evaluation results. For practical application, the Chengdu-Chongqing urban agglomeration was selected as a case study to evaluate the resilience of its transportation systems. Building on this, this research further investigates the evolving characteristics and patterns of urban transportation resilience, aiming to provide valuable insights for resilience research and strategic planning in urban transportation. The results indicate that an overall upward trend in the transportation resilience of the Chengdu-Chongqing area from 2012 to 2022, and presents a double-peak structure in 2022. Resilience characteristics within the agglomeration took various forms, such as ''pyramid,'' and ''core-edge.'' Throughout the period, the resilience exhibited α-convergence, while the spatial distribution displayed a negative spatial correlation. Moreover, when accounting for spatial correlations, a significant absolute β-convergence trend in resilience was observed. |
| format | Article |
| id | doaj-art-49fb5e71ed3a45a5ba0f3ffe3b861283 |
| institution | DOAJ |
| issn | 2665-9727 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental and Sustainability Indicators |
| spelling | doaj-art-49fb5e71ed3a45a5ba0f3ffe3b8612832025-08-20T03:07:11ZengElsevierEnvironmental and Sustainability Indicators2665-97272025-06-012610071410.1016/j.indic.2025.100714Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven modelZihao Guo0Xuanling Zhou1Cong Lu2Shixin Li3Yunlong Zhu4Yinhang Liu5Zhijian Li6College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China; Corresponding author.College of Foreign Studies, Nanjing University, Nanjing, 210023, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing, 211816, China; Smart City Research Center, Nanjing Tech University, Nanjing, 211816, ChinaCollege of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing, 211816, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing, 211816, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing, 211816, ChinaAs global urbanization accelerates, urban transportation systems increasingly face challenges from natural disasters and public safety issues. Resilience in urban transportation is essential for sustainable urban development. This study proposes an innovative, data-driven approach to quantify urban transportation resilience, integrating genetic algorithms (GA), backpropagation (BP) neural networks, and the entropy weighting method. This approach aims to eliminate the introduction of subjective biases by experts in traditional methods, providing a more accurate and objective evaluation results. For practical application, the Chengdu-Chongqing urban agglomeration was selected as a case study to evaluate the resilience of its transportation systems. Building on this, this research further investigates the evolving characteristics and patterns of urban transportation resilience, aiming to provide valuable insights for resilience research and strategic planning in urban transportation. The results indicate that an overall upward trend in the transportation resilience of the Chengdu-Chongqing area from 2012 to 2022, and presents a double-peak structure in 2022. Resilience characteristics within the agglomeration took various forms, such as ''pyramid,'' and ''core-edge.'' Throughout the period, the resilience exhibited α-convergence, while the spatial distribution displayed a negative spatial correlation. Moreover, when accounting for spatial correlations, a significant absolute β-convergence trend in resilience was observed.http://www.sciencedirect.com/science/article/pii/S2665972725001357Urban transportationResilience evaluationUrban agglomerationGenetic algorithmBP neural networkChina |
| spellingShingle | Zihao Guo Xuanling Zhou Cong Lu Shixin Li Yunlong Zhu Yinhang Liu Zhijian Li Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model Environmental and Sustainability Indicators Urban transportation Resilience evaluation Urban agglomeration Genetic algorithm BP neural network China |
| title | Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model |
| title_full | Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model |
| title_fullStr | Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model |
| title_full_unstemmed | Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model |
| title_short | Urban agglomeration transportation resilience: evaluation and evolution analysis using a data-driven model |
| title_sort | urban agglomeration transportation resilience evaluation and evolution analysis using a data driven model |
| topic | Urban transportation Resilience evaluation Urban agglomeration Genetic algorithm BP neural network China |
| url | http://www.sciencedirect.com/science/article/pii/S2665972725001357 |
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