Quantifying the impact of extreme weather event on intercity mobility in urban agglomerations: A Bayesian structural time series analysis
文献类型：期刊论文
| 作者 | Li, Tao2; Wei, Shanshan2; Sun, Yan2; Du, Fangye1; Wang, Jiaoe3,4 |
| 刊名 | TRANSPORT POLICY
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 176页码:103925 |
| 关键词 | Intercity mobility Bayesian structural time series (BSTS) Urban agglomeration Resilience Heavy rainfall |
| ISSN号 | 0967-070X |
| DOI | 10.1016/j.tranpol.2025.103925 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Understanding the spatiotemporal dynamics of human mobility responses to extreme weather events holds critical significance for developing climate adaptation strategies and fortifying socio-ecological system resilience. Despite proliferating intra-city mobility studies, comprehensive investigations of intercity mobility pattern under compound weather events remain insufficient in an era of escalating climate-related disasters. To fill this gap, this paper employs the Bayesian Structural Time Series model to infer the intercity mobility impact of heavy rainfall event. The Zhengzhou 7.20 heavy rainfall in 2021 in the Central Plains Urban Agglomeration (CPUA) of China is selected as a case study. The results indicated a significant rainfall-induced disruption to intercity mobility. Outflows and inflows declined by 30.65 % and 25.92 % on average, respectively. Spatially, impacts exhibited pronounced geographical congruence, with peak effects concentrated in Zhengzhou and adjacent cities. The greater disruption to outflows (vs. inflows) indicates asymmetric network resilience. For Zhengzhou, outflows showed abrupt decline-rapid rebound dynamics, while inflows demonstrated rapid initial drop followed by extended gradual recovery. Conversely, other CPUA cities exhibited gradual recovery trajectories for both inflows and outflows. Cities with stronger pre-event social connectivity (measured by baseline mobility) experienced smaller mobility reductions, while betweenness centrality reflecting a city's intermediary capacity within high speed rail network correlated positively with relative impact magnitude. These findings provide actionable insights for designing climate-resilient transportation networks in metropolitan regions. |
| URL标识 | 查看原文 |
| WOS关键词 | COVID-19 ; RECOVERY ; TRAVEL |
| WOS研究方向 | Business & Economics ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001630545700003 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219761]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Jiaoe |
| 作者单位 | 1.Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Peoples R China; 2.Shaanxi Normal Univ, Sch Geog & Tourism, Xian 710119, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Li, Tao,Wei, Shanshan,Sun, Yan,et al. Quantifying the impact of extreme weather event on intercity mobility in urban agglomerations: A Bayesian structural time series analysis[J]. TRANSPORT POLICY,2026,176:103925. |
| APA | Li, Tao,Wei, Shanshan,Sun, Yan,Du, Fangye,&Wang, Jiaoe.(2026).Quantifying the impact of extreme weather event on intercity mobility in urban agglomerations: A Bayesian structural time series analysis.TRANSPORT POLICY,176,103925. |
| MLA | Li, Tao,et al."Quantifying the impact of extreme weather event on intercity mobility in urban agglomerations: A Bayesian structural time series analysis".TRANSPORT POLICY 176(2026):103925. |
入库方式： OAI收割
来源：地理科学与资源研究所
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