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Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes
Journal of Safety Research ( IF 4.264 ) Pub Date : 2021-09-23 , DOI: 10.1016/j.jsr.2021.09.005
Seyed Alireza Samerei 1 , Kayvan Aghabayk 1 , Nirajan Shiwakoti 2 , Amin Mohammadi 3
Affiliation  

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.



中文翻译:

使用潜在类别聚类和二元逻辑回归来模拟澳大利亚骑自行车者在机动车-自行车碰撞中的伤害严重程度

简介:近年来,澳大利亚骑自行车的人总数正在增加。然而,骑自行车者严重受伤和死亡人数的增加一直是一个主要问题。了解影响骑车人在与机动车相撞中伤亡的因素对于制定旨在提高骑车人安全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辆-自行车 (MVB) 碰撞事故的因素,并针对已确定的风险因素引入有效的对策。方法:使用二元 logit 模型和潜在类聚类分析了 2006 年至 2019 年间来自澳大利亚维多利亚州的 14,759 条 MVB 崩溃记录的数据集。结果:据观察,在所有集群中增加骑自行车者(FSI)死亡和重伤风险的因素是:老年骑自行车者、不使用头盔和黑暗条件。同样,在没有交通管制、天气晴朗和地面干燥的地区(第 1 类),高速限制会增加 FSI 的风险,但在十字路口和 T 形路口发生 MVB 碰撞与减少交通事故显着相关。在 FSI 的风险中。在有交通管制和不利天气条件的地区(第 2 类),潮湿的路面会增加 FSI 的风险,但有让路标志和人行横道标志的区域会降低 FSI 的风险。实际应用: 减少骑车人死亡或重伤风险的建议是:改善道路照明,让骑车人更多地使用反光衣和反光板,在中间街区特别是在高速区域分隔自行车和车辆路径,使用更多为老年人提供稳定的自行车,监控头盔使用情况,改进自动紧急制动,并为车辆使用骑车人检测技术。

更新日期:2021-11-27
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