Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, China.
Jiangsu Province Collaborative Innovation Center of Modem Urban Traffic Technologies, Southeast University, Nanjing, China.
Int J Inj Contr Saf Promot. 2024 Mar;31(1):138-147. doi: 10.1080/17457300.2023.2272242. Epub 2023 Oct 24.
The distraction affects driving performance and induces serious safety issues. To better understand distracted driving, this study examines the influence of distracted driving on overall driving performance. This paper analyzes the distraction behavior (mobile phone use, entertainment activities, and passenger interference) under three driving tasks. The statistical results show that viewing or sending messages is common during driving. Smoking, phone calls, and talking to passengers are evident in cruising, ride request and drop-off, respectively. Then, overall driving performance is proposed based on velocity, longitudinal acceleration (longacc) and yaw_rate. It is divided into three categories, high, medium, and low, by k-means algorithms. The average speed increases from low to high performance; however, the longacc and yaw_rate decrease. Finally, the influence of distracted driving on overall driving performance is analyzed using C4.5 algorithm. The result shows that when time is peak, the probability of high performance (HP) is higher than off-peak. The possibility of HP increases with the increase of duration; the number of, talking to passengers, listening to music or radio, eating; the duration of, viewing or sending messages, phone calls; but reduces with the increase of the number of phone calls. These findings provide theoretical support for driving performance evaluation.
分心会影响驾驶表现，并引发严重的安全问题。为了更好地理解分心驾驶，本研究考察了分心驾驶对整体驾驶表现的影响。本文分析了三种驾驶任务下的分心行为（使用手机、娱乐活动和乘客干扰）。统计结果表明，驾驶过程中普遍存在查看或发送信息的行为。在巡航、乘车请求和下车过程中，分别存在吸烟、打电话和与乘客交谈的行为。然后，基于速度、纵向加速度（longacc）和偏航率提出了整体驾驶表现。通过 k-means 算法将其分为高、中、低三个类别。平均速度从低到高性能增加；然而，longacc 和 yaw_rate 降低。最后，使用 C4.5 算法分析了分心驾驶对整体驾驶性能的影响。结果表明，当时间处于高峰期时，高性能（HP）的概率高于非高峰期。随着时间的增加，HP 的可能性增加；与乘客交谈、听音乐或广播、进食的次数；查看或发送消息、打电话的持续时间；但随着电话数量的增加而减少。这些发现为驾驶性能评估提供了理论支持。
