自行车事故严重程度影响因素的时空效应

Spatial and Temporal Effects of Factors Influencing Severity of Bicycle Accidents

摘要

为探究事故成因的时间特征和空间特征及自行车事故的致因机理,提升城市道路交通安全管理水平,基于现有空间模型引入时间维度,通过采用时空聚类分析及时空地理加权回归模型,选取北卡罗来纳州2007—2019年的自行车事故数据,对事故严重程度影响因素的时空分布规律、潜在未观察到的异质性及各因素在不同时空下对事故严重程度的影响趋势进行探究,旨在针对性地降低局部及全局交通事故的发生概率。结果表明:考虑季节性特征的核密度估计中,冬季事故呈现高发态势;在探究不同季节下的空间聚类分析中,事故严重程度在时间及空间上存在非平稳性,形成了在韦克(Wake)、德罕(Durham)等6个地区的夏季、冬季高/低聚集分布;在季节为时间变量的条件下,24个解释变量中事故双方饮酒或吸毒、道路限速、肇事逃逸、骑行者年龄在55岁以上、呼叫救护车及曲线型道路等18个因素在春夏季对事故严重程度存在显著积极的影响,时空地理加权回归模型对于数据的时空异质性处理良好。研究对交通安全及管理的相关从业者提供了合理的理论依据,对改善自行车骑行者的出行安全起到了一定作用。

Abstract

To explore the temporal and spatial characteristics of accident causes and the driving principles of bicycle accidents, and to improve urban roadway traffic safety management, by using geographically and temporally cluster analysis and geographically and temporally weighted regression models, bicycle accident data from 2007—2019 in North Carolina were selected to explore the geographically and temporally distribution patterns of factors influencing the severity of accidents, the potentially unobserved heterogeneity, and the trend of the influence of each factor on the severity of accidents under different spatial and temporal conditions, aiming at targeting the reduction of both local and global traffic accident probability. Results show that in the kernel density estimation considering seasonal characteristics, winter accidents show a high incidence; in the spatial cluster analysis exploring different seasons, accident severity is non-stationary in time and space, forming the high/low clustered and the hotspot clustering distribution in summer and winter in six regions including Wake and Durham. Under the condition of season as a time variable, the eighteen of the 24 explanatory variables, including alcohol or drug use by both parties to the accident, high road speed limit, hit-run, rider age over 55, ambulance call, and curved road, had significant positive effects on accident severity, and the geographically and temporally weighted regression model handled the spatial-temporal heterogeneity of the data well. Due to the combined effects of multiple influencing factors in diverse spatiotemporal contexts, aggregation exhibits differentiated distribution patterns. Based on the above findings, this paper investigates the underlying formation mechanisms and formulates targeted policy suggestions to enhance the traffic safety level of vulnerable road groups. The study provides a reasonable theoretical basis for practitioners related to traffic safety and management, and plays a role in improving the travel safety of bicyclists.

关键词

交通工程 / 交通安全 / 时空异质性 / 时空地理加权回归模型 / 自行车事故 / 聚类分析

Key words

traffic engineering / road safety / spatial-temporal heterogeneity / geographically and temporally weighted regression models / bicycle accident / cluster analysis

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基金资助

教育部人文社会科学研究青年基金资助项目(21YJC630094)

北京科协青年人才托举工程资助项目(BYESS2024109)