熟悉和不熟悉驾驶员的人车碰撞中行人伤害严重程度分析

IF 3.6 2区 工程技术 Q2 TRANSPORTATION
Gang Xue , Huiying Wen
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引用次数: 0

摘要

行人与车辆碰撞中的行人伤害与驾驶员、行人、车辆、碰撞和环境特征有很大关系。驾驶员对路线的熟悉程度与驾驶行为有很大关系。本研究采用混合 logit 模型,对云南省两年的行人与车辆碰撞数据进行了研究,以探究在与熟悉和不熟悉的驾驶员发生碰撞时,影响行人受伤严重程度的因素。结果发现,有 8 个变量仅在熟悉司机模型中显著。六个变量仅在不熟悉的驾驶员模型中显著。估计结果表明,在熟悉驾驶员模型中,将清晨和晴朗天气条件因素作为随机参数建模效果更好,而在不熟悉驾驶员模型中,将阴雨天气条件和下午高峰期因素作为随机参数建模效果也更好。为熟悉情况的司机、不熟悉情况的司机和交通管理人员提出了一些更有效、更有针对性的对策,以降低行人受伤的严重程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pedestrian-injury severity analysis in pedestrian-vehicle crashes with familiar and unfamiliar drivers

Pedestrian injury in pedestrian-vehicle crash is significantly related to the driver, pedestrian, vehicle, crash and environment characteristics. Driver’s route familiarity has been found greatly associated with driving behaviours. Two-year pedestrian-vehicle crash data in Yunnan Province were studied to investigate the factors that affect pedestrian-injury severities in crashes with familiar and unfamiliar drivers by employing mixed logit models. Eight variables were found significant only in the familiar driver model. And six variables were found significant only in the unfamiliar driver model. Estimation findings indicate that the factors of early morning and sunny weather condition will be better modelled as random parameters in the model for familiar drivers and the same with the factors of rainy weather condition and afternoon peak in the model for unfamiliar drivers. Some more effective and targeted countermeasures are put forward for familiar drivers, unfamiliar drivers and transportation managers to reduce pedestrians’ injury severities.

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来源期刊
Transportmetrica A-Transport Science
Transportmetrica A-Transport Science TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
8.10
自引率
12.10%
发文量
55
期刊介绍: Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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