Influence of road environmental elements on pedestrian and cyclist road crossing behaviour

Yuichi Saito, Fuma Kochi, M. Itoh, T. Fushima, Takashi Sugano, Yasunori Yamamoto
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引用次数: 1

Abstract

The pedestrian and cyclist-related accident fatality rate is higher than that of other traffic accidents. One of the pedestrian behaviours that leads to traffic accidents is the act of moving rapidly onto the road from a blind spot without warning. Expert drivers practice hazard-anticipatory driving and will naturally seek to reduce uncertainty by attempting to fit their current driving context into a pre-existing category. Risk management is the process of identifying hazards and assessing and controlling risks to attain safety. The purpose of this study was to evaluate the influence that driving context-altering road environmental elements exert on the road-crossing behaviour of pedestrians and cyclists. Thus, this study attempted to identify covert hazards (obscured pedestrians and cyclists). A logistic regression analysis was employed along with data from the near-miss incident database, in which approximately 140,000 near-crash-relevant events were registered in 2017. By using the logistic regression analysis along with the annotations recorded in the database, we constructed a predictive model to identify covert hazards. The study demonstrated the feasibility of using a set of environmental elements that shape the driving context to construct a predictive model that identifies covert hazards.
道路环境因素对行人和骑自行车者过马路行为的影响
与行人和骑自行车相关的交通事故死亡率高于其他交通事故。导致交通事故的行人行为之一是在没有警告的情况下从盲点迅速移动到道路上。经验丰富的司机会进行危险预估驾驶,自然会通过尝试将当前的驾驶环境与预先存在的类别相匹配来减少不确定性。风险管理是识别危害、评估和控制风险以达到安全的过程。本研究的目的是评估驾驶情境改变的道路环境因素对行人和骑自行车者过马路行为的影响。因此,本研究试图识别隐蔽的危险(被遮挡的行人和骑自行车的人)。该研究采用了逻辑回归分析以及来自未遂事故数据库的数据,该数据库在2017年记录了大约14万起与未遂事故相关的事件。通过逻辑回归分析以及数据库中记录的注释,我们构建了一个预测模型来识别隐蔽危险。该研究证明了使用一组影响驾驶环境的环境因素来构建识别隐蔽危险的预测模型的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.20
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