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采用潜在类别聚类和二元逻辑回归模型分析澳大利亚机动车-自行车碰撞中自行车骑手损伤严重程度。

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes.

机构信息

School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.

School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.

出版信息

J Safety Res. 2021 Dec;79:246-256. doi: 10.1016/j.jsr.2021.09.005. Epub 2021 Sep 23.

DOI:10.1016/j.jsr.2021.09.005
PMID:34848005
Abstract

INTRODUCTION

In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors.

METHOD

A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering.

RESULTS

It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.

摘要

简介

近年来,澳大利亚的自行车骑行者总数有所增加。然而,自行车骑行者重伤和死亡人数的上升一直是一个主要关注点。了解影响与机动车碰撞的自行车骑行者伤亡的因素对于制定旨在提高自行车骑行者安全性的有效政策措施非常重要。本研究旨在确定影响澳大利亚维多利亚州机动车与自行车(MVB)碰撞的因素,并针对确定的风险因素引入有效对策。

方法

使用二元逻辑模型和潜在类别聚类分析了 2006 年至 2019 年期间澳大利亚维多利亚州的 14759 份 MVB 碰撞记录数据集。

结果

观察到所有聚类中增加自行车骑行者(FSI)死亡和重伤风险的因素是:老年自行车骑行者、不戴头盔和黑暗条件。同样,在无交通管制、天气晴朗和干燥路面条件的区域(聚类 1),高速限制会增加 FSI 的风险,但在十字交叉口和 T 型交叉口发生的 MVB 碰撞与 FSI 风险降低显著相关。在有交通管制和不利天气条件的区域(聚类 2),湿路面会增加 FSI 的风险,但让行标志和行人横道标志的区域会降低 FSI 的风险。

实际应用

减少自行车骑行者死亡或重伤风险的建议是:改善道路照明,更多地暴露使用反光服装和反光镜的自行车骑行者,在中间路段特别是在高速区域分离自行车和车辆道,为老年人使用更稳定的自行车,监测头盔使用情况,改进自动紧急制动,并为车辆使用自行车骑行者检测技术。

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