Hjelkrem Odd André, Ryeng Eirin Olaussen
Department of Civil and Transport Engineering, The Norwegian University of Science and Technology, NO-7491 Trondheim, Norway.
Accid Anal Prev. 2016 Oct;95(Pt A):227-35. doi: 10.1016/j.aap.2016.07.006. Epub 2016 Jul 25.
This study examines how precipitation, light conditions and surface conditions affect the drivers' risk perception. An indicator CRI (Chosen Risk Index) is defined, which describes the chosen risk level for drivers in a car-following situation. The dataset contains about 70 000 observations of driver behaviour and weather status on a rural road. Based on the theory of risk homeostasis and an assumption that driving behaviour in situations with daylight, dry road and no precipitation reflects drivers' target level of risk, generalised linear models (GLM) were estimated for cars and trucks separately to reveal the effect of adverse weather conditions on risk perception. The analyses show that both car and truck drivers perceive the highest risk when driving on snow covered roads. For car drivers, a snow covered road in combination with moderate rain or light snow are the factors which lowers the CRI the most. For trucks, snow cover and partially covered roads significantly lowers the CRI, while precipitation did not seem to impose any higher risk. Interaction effects were found for car drivers only.
本研究考察了降水、光照条件和路面状况如何影响驾驶员的风险感知。定义了一个指标CRI（选定风险指数），它描述了跟车情况下驾驶员选定的风险水平。该数据集包含约70000条乡村道路上驾驶员行为和天气状况的观测数据。基于风险稳态理论，并假设在白天、干燥路面且无降水的情况下的驾驶行为反映了驾驶员的目标风险水平，分别对汽车和卡车估计了广义线性模型（GLM），以揭示不利天气条件对风险感知的影响。分析表明，汽车和卡车驾驶员在积雪道路上行驶时都感知到最高风险。对于汽车驾驶员来说，积雪道路加上中雨或小雪是使CRI降低最多的因素。对于卡车来说，积雪和部分覆盖的道路会显著降低CRI，而降水似乎并未带来更高风险。仅在汽车驾驶员中发现了交互作用。
