Assessing urban emergency medical services accessibility for older adults considering ambulance trafficability using a deep learning approach

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-09-15 DOI:10.1016/j.scs.2025.106804

Zhuo Liu , Enjia Zhang , Shuo Pan , Sichun Li , Ying Long , Frank Witlox

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引用次数: 0

Abstract

Rapid urbanization and population aging have made equitable Emergency Medical Services (EMS) access for older adults a critical challenge in high-density cities. This study develops a deep learning framework to evaluate EMS accessibility, considering ambulance trafficability derived from street view images (SVIs). A Multi-Scale Vision Transformer (MSViT) model classifies SVIs into impassable, narrow, and passable categories to assess road conditions. Travel speeds are then assigned based on the classified trafficability and road hierarchy data. The framework measures the pre-hospital time through two-stage travel (emergency center-to-patient and patient-to-hospital), while evaluating accessibility through two complementary metrics: the inverse of the shortest total pre-hospital time, and a composite accessibility score with Gaussian time-decay weighting for all facilities within 15-minute service ranges. A case study in Beijing’s Old City demonstrated that considering ambulance trafficability reduces the estimated 15-minute coverage from 94.18 % to 83.14 %, revealing significant overestimation when road conditions are neglected. Spatially, peripheral areas achieved better nearest-facility response times, whereas central regions dominated in comprehensive service coverage. Additionally, EMS accessibility patterns strongly correlated with older adults’ distribution, showing limited income-based disparities. This study shifts the focus from supply-demand balancing to road system management in EMS accessibility, which can be integrated with existing methods to support more sustainable and targeted infrastructure optimization in aging cities.

查看原文本刊更多论文

使用深度学习方法评估老年人城市紧急医疗服务的可及性，考虑救护车的可通行性

快速城市化和人口老龄化使得老年人公平获得紧急医疗服务（EMS）成为高密度城市的一项重大挑战。本研究开发了一个深度学习框架来评估EMS的可达性，并考虑了来自街景图像（svi）的救护车可通行性。多尺度视觉变换（MSViT）模型将svi分为不可通行、狭窄通行和可通行三类，以评估道路状况。然后根据分类的交通能力和道路层次数据分配行驶速度。该框架通过两阶段旅行（急救中心到病人和病人到医院）来衡量院前时间，同时通过两个互补指标来评估可达性：最短总院前时间的倒数，以及15分钟服务范围内所有设施的高斯时间衰减加权的复合可达性得分。在北京老城区进行的一项案例研究表明，考虑救护车的可通行性会使估计的15分钟覆盖率从94.18%降低到83.14%，这表明在忽略道路条件的情况下，估计过高。从空间上看，外围地区最近设施的响应时间更好，而中心地区在综合服务覆盖方面占主导地位。此外，EMS可及性模式与老年人的分布密切相关，显示出有限的收入差异。本研究将重点从EMS可达性的供需平衡转向道路系统管理，并与现有方法相结合，支持老龄化城市更可持续、更有针对性的基础设施优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;