Zachry Department of Civil and Environmental Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843-3136, United States.
Center for Transportation Safety, Texas A&M Transportation Institute, 3135 TAMU, College Station, Texas, 77843-3135, United States.
J Safety Res. 2021 Dec;79:321-328. doi: 10.1016/j.jsr.2021.09.013. Epub 2021 Oct 13.
Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes.
The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances.
RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.
鉴于大量生命伤亡，分心驾驶是一个重要的研究安全领域。随着手机的广泛使用，开车时打电话已成为最常见的分心驾驶行为。尽管研究人员已经为各种碰撞类型开发了安全性能函数 (SPF)，但文献中很少研究分心驾驶影响的碰撞的 SPF。一个可能的原因是常用安全数据（即道路清单、交通和碰撞计数）中缺乏关键的分心行为信息。最近，手机使用频率（称为手机使用数据）由移动应用程序公司记录，并可供安全研究人员使用。本研究的主要目的是检验手机使用数据是否有可能预测分心驾驶影响的碰撞。
作者首先将手机使用数据与德克萨斯州的道路清单、交通和碰撞数据集成。然后，随机森林 (RF) 算法被应用于评估特征 - 驾驶时使用手机 - 对预测道路段分心驾驶影响的碰撞数量的重要性。此外，本研究分别使用和不使用手机使用数据为分心驾驶影响的碰撞开发了两个 SPF。这两个 SPF 都从模型拟合和预测性能方面进行了评估。
RF 结果将手机使用频率列为导致分心驾驶影响的碰撞数量的重要因素之一。性能评估表明，将手机使用数据纳入 SPF 中可一致提高预测分心驾驶影响的碰撞的拟合和预测能力。
手机使用数据为分心驾驶影响的碰撞安全分析提供了新的见解，仅使用传统的道路清单和碰撞数据是无法实现的。因此，鼓励安全研究人员和从业人员在减少分心驾驶影响的碰撞中纳入新兴数据来源。
