Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
This work provides insights into urban dynamics for planners and policymakers, but it is incremental as it applies an existing method to new mobility data.
The study quantified periodicity in human mobility data using sparse auto-correlation identification and applied it to metro and multi-modal data in cities like Hangzhou, New York, and Chicago, revealing weekly periodicity and pandemic impacts on ridesharing from 2019 to 2024.
Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility data as a sparse identification of dominant positive auto-correlations in time series autoregression and then discovers periodic patterns. We apply the framework to large-scale metro passenger flow data in Hangzhou, China and multi-modal mobility data in New York City and Chicago, USA, revealing the interpretable weekly periodicity across different spatial locations over past several years. The analysis of ridesharing data from 2019 to 2024 demonstrates the disruptive impact of the pandemic on mobility regularity and the subsequent recovery trends. In 2024, the periodic mobility patterns of ridesharing, taxi, subway, and bikesharing in Manhattan uncover the regularity and variability of these travel modes. Our findings highlight the potential of interpretable machine learning to discover spatiotemporal mobility patterns and offer a valuable tool for understanding urban systems.