Transportation Informatics Lab, Department of Civil & Environmental Engineering, Old Dominion University, 129C Kaufman Hall, Norfolk, VA 23529, USA; Civil Engineering Department, Jouf University, Sakaka 72388, Saudi Arabia.
Transportation Informatics Lab, Department of Civil & Environmental Engineering, Old Dominion University, 129C Kaufman Hall, Norfolk, VA 23529, USA.
Accid Anal Prev. 2024 May;199:107514. doi: 10.1016/j.aap.2024.107514. Epub 2024 Feb 23.
Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.
配备先进传感器并能够向驾驶员传达安全信息的联网车辆 (CV) 有可能减少事故。本研究的目的是评估 CV 技术在不同天气条件下（包括晴天和雾天）高速公路碰撞场景中对驾驶行为和安全结果的影响。进行了驾驶模拟器实验，并采用多组结构方程建模 (SEM) 来探索交通冲突倾向、CV 警报的使用、天气、心理因素、驾驶行为和其他相关变量之间的复杂相互关系，这两个变量适用于两个不同的碰撞位置，即直道和水平曲线。两个潜在的心理因素包括攻击性和无意识性，它们是从车辆经过刹车、油门、转向角度、车道偏离和偏航等碰撞场景的驾驶行为中构建的。SEM 可以通过单个统计估计过程同时测量潜在的心理因素和模型相互关系。多组 SEM 的结果表明，CV 警报可以显著降低水平曲线上的无意识性，从而降低交通冲突的倾向。此外，尽管雾天可能会提高情境意识，但在水平曲线上，雾天对冲突的整体影响是积极的。相比之下，通过汇总两个碰撞位置的数据，单组 SEM 在其结构模型中未能揭示任何显著的相互关系。所获得的见解可以指导驾驶辅助系统的开发，强调考虑天气条件和特定位置因素进行定制的必要性。
