Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems

TL;DR

This paper presents a data-driven framework to identify and analyze periodic patterns in urban human mobility data, revealing insights into regularity, disruptions, and recovery trends across different cities and transportation modes.

Contribution

It introduces a novel sparse auto-correlation method for discovering interpretable periodicity in large-scale mobility data, applied to multiple cities and transport modes.

Findings

Weekly mobility periodicity is consistent across different urban locations.

The pandemic caused significant disruptions to mobility regularity.

Mobility patterns are recovering and show mode-specific variability in 2024.

Abstract

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.