Towards a human-centric city emergency response: Modelling activity patterns of urban population

Human-centric management is emerging as a new trend in urban emergency response, which develops management and resource allocation strategies based on activity patterns of urban population and their derived demands. This study aims to construct an MDCEV-based model to capture the activity patternss of different types of residents during urban emergencies. Using a case study in Shanghai, China, the study calibrates and validates the model using resident survey data. In addition, we conducted scenario analyses to explore the impact of promoting community service participation, remote work experiences, and various working patterns on residents’ activity patterns. The research discusses the heterogeneity of time allocation patterns among different resident types in urban emergency management contexts and highlights the influence of external interventions on resident activities. Our findings contribute to the development of supporting measures for vulnerable residents and human-centric city emergency response strategies. • Models resident activity patterns in urban emergencies using MDCEV-based approach. • Analyses the impact of emergency on residents' time allocation. • Explores remote work, community service, and working patterns during emergency. • Reveals heterogeneity in activity patterns across different resident groups. • Proposes strategies to enhance human-centric emergency management for vulnerable populations.

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Publication Details

Journal
Developments in the Built Environment
Published
2025-02-28
DOI
https://doi.org/10.1016/j.dibe.2025.100633
Citations
17
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
37.00

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article

Towards a human-centric city emergency response: Modelling activity patterns of urban population

Yuting Hou, Izzy Yi Jian, Xuan Liu, Yibin Li et al.
17 citations
Developments in the Built Environment
Human Mobility and Location-Based Analysis
37.00
article

Towards a human-centric city emergency response: Modelling activity patterns of urban population

Yuting Hou, Izzy Yi Jian, Xuan Liu, Yibin Li, Ping He, Qian-Cheng Wang
article en
17 citations

Abstract

Human-centric management is emerging as a new trend in urban emergency response, which develops management and resource allocation strategies based on activity patterns of urban population and their derived demands. This study aims to construct an MDCEV-based model to capture the activity patternss of different types of residents during urban emergencies. Using a case study in Shanghai, China, the study calibrates and validates the model using resident survey data. In addition, we conducted scenario analyses to explore the impact of promoting community service participation, remote work experiences, and various working patterns on residents’ activity patterns. The research discusses the heterogeneity of time allocation patterns among different resident types in urban emergency management contexts and highlights the influence of external interventions on resident activities. Our findings contribute to the development of supporting measures for vulnerable residents and human-centric city emergency response strategies. • Models resident activity patterns in urban emergencies using MDCEV-based approach. • Analyses the impact of emergency on residents' time allocation. • Explores remote work, community service, and working patterns during emergency. • Reveals heterogeneity in activity patterns across different resident groups. • Proposes strategies to enhance human-centric emergency management for vulnerable populations.

Developments in the Built EnvironmentVol. 21
Hong Kong Polytechnic University (HK), University of Cambridge (GB), Education University of Hong Kong (HK), Eindhoven University of Technology (NL)
Cambridge Trust, Hong Kong Polytechnic University, Education University of Hong Kong, Henry Lester Trust
Sustainable cities and communities
Openalex Percentile: Top 0%
Human Mobility and Location-Based Analysis
37.00
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