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.