Automatic identification of bottlenecks for ambulance passage on urban streets: A deep learning-based approach

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shuo Pan , Zhuo Liu , Hai Yan , Ning Chen , Xiaoxiong Zhao , Sichun Li , Frank Witlox
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引用次数: 0

Abstract

Urban streets exhibit a diverse range of characteristics, with some presenting significant challenges to ambulance passage, directly impacting the safety of residents. Thus, ensuring unimpeded passage for ambulances on streets is a key focus of urban renewal and street governance initiatives. However, the identification of bottlenecks for emergency vehicle passage on urban streets currently relies on labor-intensive and inefficient on-site manual audits. This study proposes a deep learning-based approach to achieve automatic identification of ambulance passage on urban streets. The Vision Transformer network is utilized to construct the classification model of Impassable Narrow Roads, Passable Narrow Roads, and Wide Roads based on street view images. To train and test the constructed models, a specialized dataset is established, consisting of street view images labeled by experienced ambulance drivers. Comparative experiments are conducted to confirm the optimal structure of the model and the necessity of semantic segmentation preprocessing for street view images. To confirm the superiority of the proposed approach, four commonly used deep learning methods, MobileNet, ShuffleNet, SuperViT and DualViT serve as the baseline tests. Experimental results reveal that the model with four-head and one sequential encoder achieves the highest evaluation accuracy at 75.65% among the proposed models on the original dataset, significantly outperforming benchmark models. Meanwhile, the segmentation of street view images improves accuracy to 77.42%, but it reduces computational efficiency from 0.01 to 3 seconds per image. Finally, the optimal model is applied to the area within the Second Ring Road of Beijing as an example to discuss how the deep learning-based approach proposed in this paper supports urban planning practice and emergency medical response. The proposed approach facilitates the rapid and large-scale identification of bottlenecks in urban streets for ambulances with very limited costs, making a significant contribution to the accurate identification of key areas for urban renewal and street governance efforts. The proposed method can further assist emergency vehicle dispatchers and drivers in identifying accessible routes with greater precision during operations, thereby enabling more timely transportation of patients to medical facilities.
自动识别城市道路上救护车通行的瓶颈:基于深度学习的方法
城市街道呈现出多种多样的特点,其中一些给救护车通行带来了巨大挑战,直接影响到居民的安全。因此,确保救护车在街道上畅通无阻是城市改造和街道治理行动的重点。然而,目前城市街道急救车通行瓶颈的识别主要依赖于劳动密集型和低效的现场人工审核。本研究提出了一种基于深度学习的方法,以实现自动识别城市街道上的救护车通行情况。利用视觉转换器网络,基于街景图像构建了不可通行的狭窄道路、可通行的狭窄道路和宽阔道路的分类模型。为了训练和测试所构建的模型,建立了一个专门的数据集,由经验丰富的救护车司机标注的街景图像组成。通过对比实验确认了模型的最佳结构以及对街景图像进行语义分割预处理的必要性。为了证实所提方法的优越性,四种常用的深度学习方法:MobileNet、ShuffleNet、SuperViT 和 DualViT 作为基线测试。实验结果表明,在原始数据集上,采用四头和一个顺序编码器的模型达到了 75.65% 的最高评估准确率,明显优于基准模型。同时,街景图像分割的准确率提高到 77.42%,但计算效率却从每幅图像 0.01 秒降低到 3 秒。最后,以北京二环路以内区域为例,讨论了本文提出的基于深度学习的方法如何支持城市规划实践和紧急医疗响应。所提出的方法有助于以非常有限的成本快速、大规模地识别城市街道中的救护车瓶颈,为准确识别城市更新和街道治理工作的重点区域做出了重要贡献。所提出的方法还能进一步帮助急救车调度员和驾驶员在行动中更精确地识别无障碍路线,从而更及时地将病人运送到医疗设施。
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
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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