Postdoctoral Researcher, McMaster Institute for Transportation & Logistics (MITL), McMaster University, Hamilton, ON, Canada.
Master's Degree, Civil Engineering Department, Iran University of Science and Technology, Tehran, Iran.
Accid Anal Prev. 2020 Apr;138:105468. doi: 10.1016/j.aap.2020.105468. Epub 2020 Feb 14.
Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury and fatal crashes. A substantial portion of ROR fatalities occur in collisions with fixed objects at the roadside. Thus, this study seeks to investigate the severity of ROR crashes where elderly drivers, aged 65 years or more, hit a fixed object. The reason why the present study investigates this issue among older drivers is that, comparing to younger drivers, this age group of drivers have different psychological and physical features. Because of these differences, they are more likely to get injured in ROR types of crashes. This paper applies two types of Artificial Intelligence (AI) techniques, including hybrid Intelligent Genetic Algorithm and Artificial Neural Network (ANN) using the crashe information of California in 2012 obtained from Highway Safety Information System (HSIS) database. Although the results showed that the developed ANN outperformed the hybrid Intelligent Genetic Algorithm, the hybrid approach was more capable of predicting high-severity crashes. This is rooted in the way the hybrid model was trained by taking advantage of the Genetic Algorithm (GA). The results also indicated that the light condition has been the most significant parameter in evaluating the level of severity associated with fixed object crashes among elderly drivers, which is followed by the existence of the right and left shoulders. Following these three contributing factors, cause of collision, Average Annual Daily Traffic (AADT), number of involved vehicles, age, road surface condition, and gender have been identified as the most important variables in the developed ANN, respectively. This helps to identify gaps and improve public safety towards improving the overall highway safety situation of older drivers.
驶出路外（ROR）事故一直是一个主要关注点，因为这种类型的事故通常与相当数量的严重伤害和致命事故有关。大量的 ROR 死亡事故发生在与路边固定物体碰撞的情况下。因此，本研究旨在调查老年驾驶员（65 岁或以上）撞击固定物体的 ROR 事故的严重程度。本研究之所以调查老年驾驶员的这一问题，是因为与年轻驾驶员相比，该年龄组的驾驶员具有不同的心理和身体特征。由于这些差异，他们在 ROR 类型的事故中更有可能受伤。本文应用了两种人工智能（AI）技术，包括混合智能遗传算法和人工神经网络（ANN），使用 2012 年从加利福尼亚州高速公路安全信息系统（HSIS）数据库获得的碰撞信息。尽管结果表明，开发的 ANN 优于混合智能遗传算法，但混合方法更能预测严重程度的碰撞。这源于混合模型通过利用遗传算法（GA）进行训练的方式。结果还表明，光线条件是评估老年驾驶员与固定物体碰撞相关严重程度的最重要参数，其次是左右肩部的存在。在这三个因素之后，碰撞原因、平均年日交通量（AADT）、涉及车辆数量、年龄、路面状况和性别被确定为开发的 ANN 中的最重要变量。这有助于识别差距，提高公共安全水平，从而改善老年驾驶员的整体公路安全状况。
