Mobility Behavior Evolution During Extended Emergencies: Returners, Explorers, and the 15-Minute City

2025-09-30

AI Summary

This study investigates the dynamic evolution of urban residents’ mobility behaviors during a 15-day emergency, focusing on temporal transitions between “returners” (individuals frequently visiting a limited set of locations) and “explorers” (those exhibiting broad-spatial mobility). Method: Leveraging the high-precision YJMob100K trajectory dataset, we integrate spatiotemporal clustering, behavioral classification, and multi-scale geographic analysis to enable the first dynamic identification of long-term mobility patterns. Contribution/Results: We uncover asymmetric behavioral transitions—non-essential mobility recovers later; low-POI-density areas exhibit stronger dependence on external facilities; weekends consistently show short-distance return behavior; and ≥14 days of data are required to detect statistically significant shifts. By anchoring findings within the “15-minute city” framework, we reveal how neighborhood boundaries and weekday/weekend rhythms shape resilient mobility, and propose spatial optimization strategies for emergency response planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding human mobility during emergencies is critical for strengthening urban resilience and guiding emergency management. This study examines transitions between returners, who repeatedly visit a limited set of locations, and explorers, who travel across broader destinations, over a 15-day emergency period in a densely populated metropolitan region using the YJMob100K dataset. High-resolution spatial data reveal intra-urban behavioral dynamics often masked at coarser scales. Beyond static comparisons, we analyze how mobility evolves over time, with varying emergency durations, across weekdays and weekends, and relative to neighborhood boundaries, linking the analysis to the 15-minute city framework. Results show that at least two weeks of data are required to detect meaningful behavioral shifts. During prolonged emergencies, individuals resume visits to non-essential locations more slowly than under normal conditions. Explorers markedly reduce long distance travel, while weekends and holidays consistently exhibit returner-like, short distance patterns. Residents of low Points of Interest (POI) density neighborhoods often travel to POI rich areas, highlighting spatial disparities. Strengthening local accessibility may improve urban resilience during crises. Full reproducibility is supported through the project website: https://github.com/wissamkontar

Problem

• Analyzing human mobility transitions between returners and explorers during emergencies
• Investigating how mobility evolves over time across varying emergency durations
• Linking mobility behavior to spatial disparities and 15-minute city framework

Innovation

• Analyzing mobility transitions between returners and explorers
• Using high-resolution spatial data from YJMob100K dataset
• Linking mobility patterns to 15-minute city framework