Civil Engineering Department, Babol Noshirvani University of Technology, Babol, Iran.
J Safety Res. 2020 Feb;72:1-8. doi: 10.1016/j.jsr.2019.11.002. Epub 2019 Dec 31.
Digital billboards (DBs) are a competing factor for attracting drivers' attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers' attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers' behavior and measure factors responsible for drivers' distraction in Babol, Iran, as a case study.
Corresponding data were collected through a Naturalistic Driving Study (NDS) of 78 participants when facing DBs (1,326 samples). These data were analyzed by applying structural equation modeling (SEM) to concurrently recognize relationships between endogenous and exogenous variables. Human, environmental, and road factors were determined as exogenous latent variables in a model to evaluate their influences on drivers' distraction as an endogenous variable.
The results showed that road, environmental, and human factors reciprocally interact with drivers' distraction, although the estimated coefficient of human factors was more of a factor than that of the other groups. Furthermore, younger drivers, beginner drivers, and male drivers (as human factors); night and unclear weather like a rainy day (as environmental factors); and installing DBs at complicated traffic positions like near-intersections (as road factors) were determined to be the main factors that increase the possibility of drivers' distraction. Finally, model assessment was suggested using the goodness-of-fit indices.
数字广告牌（DB）是吸引司机注意力的竞争因素；有证据表明，DB 可能导致撞车和车辆冲突，因为它们吸引了司机的注意力。由于包括道路状况、驾驶员特征和环境因素在内的系统的复杂性，不可能确定这些因素之间的关系。因此，本研究旨在提供一种组织良好的程序，以分析 DB 对驾驶员行为的影响，并衡量伊朗博尔勒市驾驶员分心的因素，作为案例研究。
通过对 78 名参与者在面对 DB 时（1326 个样本）进行自然驾驶研究（NDS）收集相应数据。通过应用结构方程模型（SEM）对这些数据进行分析，以同时识别内源性和外源性变量之间的关系。将人为、环境和道路因素确定为模型中的外生潜在变量，以评估它们对驾驶员分心这一内生变量的影响。
结果表明，道路、环境和人为因素相互影响驾驶员的分心，但人为因素的估计系数比其他组更具影响力。此外，年轻司机、新手司机和男性司机（作为人为因素）；夜间和不清朗的天气，如雨天（作为环境因素）；以及在复杂的交通位置安装 DB，如靠近交叉口（作为道路因素），被确定为增加驾驶员分心可能性的主要因素。最后，建议使用拟合优度指数评估模型。
