用可解释方法发现城市出行的周期规律，揭示疫情前后变化。
Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
- 通过自回归分析识别出行数据中的主要周期性相关
- 发现杭州、纽约等地多年来的每周出行规律，2024年仍存在稳定模式
- 适合城市规划、交通管理及疫情后恢复研究者参考
人类出行规律对理解城市动态和辅助决策至关重要。本研究首次将复杂出行数据中的周期性量化为时间序列自回归中主导的正自相关稀疏识别，并据此发现周期模式。该框架应用于中国杭州大规模地铁客流数据，以及美国纽约和芝加哥的多模式出行数据，揭示了过去数年不同空间位置间可解释的每周周期性。对2019至2024年网约车数据的分析显示，疫情对出行规律造成显著扰动，并呈现后续恢复趋势。2024年曼哈顿地区网约车、出租车、地铁与共享单车的出行模式均展现出规律性与变异性。研究结果表明，可解释机器学习在发现时空出行模式方面具有潜力，为理解城市系统提供了有效工具。
原文摘要 · Abstract (English)
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.
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