Exploring fatal/severe pedestrian injury crash frequency at school zone crash hotspots: using interpretable machine learning to assess the micro-level street environment

IF 5.7 2区 工程技术 Q1 ECONOMICS
Kaihan Zhang , Reuben Tamakloe , Mengqiu Cao , Inhi Kim
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引用次数: 0

Abstract

Several countries have implemented designated school zones and installed traffic calming measures to enhance the safety of vulnerable pedestrians near schools. While macro-level built environment attributes (e.g., land use) have been widely acknowledged in relation to the role they play in urban traffic safety, the effects of micro-level streetscape characteristics on crash frequency have not been investigated to any significant extent. Moreover, the associations between these environmental features and crashes in school zones remains largely unknown. To address this issue, we first identified school zone-related crash hotspot using spatiotemporal hotspot mining on a comprehensive dataset of 20,484 pedestrian-vehicle crashes between 2017 and 2021 in Seoul, South Korea. Streetscape characteristics were analysed using street view imagery and advanced computer vision techniques to extract and classify pixel-wise visual elements. Preliminary findings reveal spatiotemporal variations in fatal and severe injury (FSI) crashes, with school zones in central commercial and industrial areas emerging as persistent crash hotspots that have remained statistically significant hotspots for 90 % of the study period. Further impact analysis using interpretable machine learning helped to uncover the non-linear relationships between both micro and macro environmental features and FSI frequency. Lower levels of street enclosure and walkability were associated with a higher frequency of FSI crashes, while increased openness and imageability were also correlated with more FSI incidents. Additionally, street greenery was found to reduce FSI crashes once it reached a certain threshold. Our findings extend existing knowledge of how the built environment and streetscape design influence pedestrian safety in school zones, paving the way for more targeted interventions to plan safer pedestrian environments around schools.
探索学校区域碰撞热点的致命/严重行人伤害碰撞频率:使用可解释的机器学习评估微观街道环境
一些国家已经实施了指定的学校区域,并安装了交通疏导措施,以加强学校附近易受伤害的行人的安全。虽然宏观层面的建筑环境属性(如土地使用)在城市交通安全中的作用已得到广泛认可,但微观层面的街景特征对交通事故频率的影响却尚未得到深入研究。此外,这些环境特征与学校区域内撞车事故之间的关系在很大程度上仍不为人所知。为解决这一问题,我们首先在韩国首尔 2017 年至 2021 年间 20484 起行人与车辆碰撞事故的综合数据集上,利用时空热点挖掘法确定了与学校区相关的碰撞事故热点。利用街景图像和先进的计算机视觉技术对街景特征进行了分析,以提取像素级视觉元素并对其进行分类。初步研究结果表明,致命和严重伤害(FSI)碰撞事故存在时空变化,中心商业区和工业区的学校区域成为持续的碰撞事故热点,在研究期间的 90% 的时间里,这些区域一直是统计意义上的热点区域。利用可解释的机器学习进行的进一步影响分析有助于发现微观和宏观环境特征与 FSI 频率之间的非线性关系。较低的街道封闭性和可步行性与较高的 FSI 碰撞频率相关,而较高的开放性和可视性也与较多的 FSI 事件相关。此外,我们还发现,街道绿化一旦达到一定的临界值,就会减少 FSI 碰撞事故。我们的研究结果扩展了关于建筑环境和街景设计如何影响学校区域行人安全的现有知识,为采取更有针对性的干预措施以规划更安全的学校周边行人环境铺平了道路。
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来源期刊
CiteScore
11.50
自引率
11.50%
发文量
197
期刊介绍: A major resurgence has occurred in transport geography in the wake of political and policy changes, huge transport infrastructure projects and responses to urban traffic congestion. The Journal of Transport Geography provides a central focus for developments in this rapidly expanding sub-discipline.
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