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

2025-08-02

AI Summary

This study addresses the challenge of automatically discovering and quantifying periodic patterns in urban transportation systems from multidimensional human mobility data, while characterizing their spatiotemporal dynamics and responses to external shocks (e.g., pandemics). Method: We propose an interpretable, data-driven framework for periodicity quantification, integrating sparse autoregressive modeling with positive autocorrelation identification, and jointly analyzing large-scale metro and ride-hailing trajectory data. Contribution/Results: Experiments across multi-year datasets from Hangzhou, New York City, and Chicago reveal spatially consistent strong weekly periodicity in all three cities. The 2020 pandemic induced significant attenuation of periodicity in NYC and Chicago, with NYC exhibiting faster recovery—demonstrating differential urban resilience. Our framework enables real-time monitoring of urban dynamics and provides a novel paradigm for quantitative, interpretable assessment of transportation system robustness and adaptability under disruption.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Application Domains: Transportation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Quantify periodicity in human mobility data
• Analyze COVID-19 impact on mobility regularity
• Compare recovery trends between NYC and Chicago

Innovation

• Sparse autoregression for periodicity quantification
• Data-driven interpretable machine learning approach
• Analyzes metro and ridesharing mobility data