Uncovering human behavioral heterogeneity in urban mobility under the impacts of disruptive weather events

Abstract

Understanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatiotemporal characteristics, causing the uncertainty problem for identifying meaningful travel behaviors. Moreover, there is a lack of robust methodological approaches to extracting stable and genuine travel patterns under normal or disruptive situations. To address these issues, this study proposes a data-driven approach to spatiotemporal flow decomposition based on non-negative matrix factorization. With sparseness factored in the decomposition, stable disaggregate travel patterns can be extracted from origin-destination mobility flows. By combining temporal, spatial, and urban functional perspectives, heterogeneous travel behaviors can be analyzed and inferred. With a case study of the Zhengzhou ‘7.20’ heavy rainfall in 2021, the most extreme rainfall ever recorded in China, this study validated the effectiveness of the proposed approach and managed to identify representative and interesting travel patterns and behaviors, facilitating a better understanding of human travel behaviors under external impacts. In practice, this study can provide valuable insights for coping strategies in the face of increasingly frequent disruptive events.

Keywords:

• Human mobility
• travel behavior
• urban resilience
• spatiotemporal flow decomposition
• Zhengzhou ‘7.20’ heavy rainfall

Acknowledgments

The authors would like to express sincere thanks to the Editor-in-Chief, Prof. May Yuan, the Guest Editor, Prof. Somayeh Dodge, and three anonymous reviewers for their insightful comments and suggestions, which have significantly enhanced the quality of this paper.

Data and codes availability statement

The data and codes that support the findings of this study are available here https://doi.org/10.6084/m9.figshare.24798189. Human movement flow data cannot be made publicly available due to privacy considerations. Instead, mocked data are shared to demonstrate how the codes work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Shenzhen Science and Technology Program under Grant [KQTD20221101093604016, JCYJ20220818100810024]; and the National Natural Science Foundation of China under Grant [41925003, 42130402].

Notes on contributors

Zhaoya Gong

Zhaoya Gong is an assistant professor at the School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, China. His research interests include geospatial artificial intelligence, urban data science and big data analytics. He contributed to the conceptualization, methodology, investigation, resources, writing – review and editing.

Zhicheng Deng

Zhicheng Deng is a master student at the School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, China. His research interests include spatiotemporal data mining and geographical information science. He contributed to the methodology, software, validation, formal analysis, visualization and writing – original draft.

Junqing Tang

Junqing Tang is an assistant professor at the School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, China. He contributed to the investigation, data curation, writing – review and editing.

Hongbo Zhao

Hongbo Zhao is an associate professor in the Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization, Henan University, Kaifeng, China. He contributed to the investigation, data curation, writing – review and editing.

Zhengying Liu

Zhengying Liu is an assistant professor at the School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen, China. He contributed to the investigation, data curation, writing – review and editing.

Pengjun Zhao

Pengjun Zhao is a full professor and the dean of School of Urban Planning and Design of Peking University. His research interests include spatial planning, and sustainable transportation. He contributed to the conceptualization, supervision and project administration.