Data-Driven Discovery of Mobility Periodicity for Understanding Urban Transportation Systems

Uncovering the temporal regularity of human mobility is crucial for discovering urban dynamics and has implications for various decision-making processes and urban system applications. This study formulates the periodicity quantification problem in complex and multidimensional human mobility data as a sparse identification of dominant positive auto-correlations in time series autoregression, allowing one to discover and quantify significant periodic patterns such as weekly periodicity from a data-driven and interpretable machine learning perspective. We apply our framework to real-world human mobility data, including metro passenger flow in Hangzhou, China and ridesharing trips in New York City (NYC) and Chicago, USA, revealing the interpretable weekly periodicity across different spatial locations over past several years. In particular, our analysis of ridesharing data from 2019 to 2024 demonstrates the disruptive impact of the COVID-19 pandemic on mobility regularity and the subsequent recovery trends, highlighting differences in the recovery pattern percentages and speeds between NYC and Chicago. We explore that both NYC and Chicago experienced a remarkable reduction of weekly periodicity in 2020, and the recovery of mobility regularity in NYC is faster than Chicago. The interpretability of sparse autoregression provides insights into the underlying temporal patterns of human mobility, offering a valuable tool for understanding urban systems. Our findings highlight the potential of interpretable machine learning to unlock crucial insights from real-world mobility data.