Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, 3 Shangyuancun, Haidian District, Beijing 100044, China.
Center for Transportation Safety, Texas A&M Transportation Institute, College Station, TX 77843-3135, United States.
Accid Anal Prev. 2023 Feb;180:106925. doi: 10.1016/j.aap.2022.106925. Epub 2022 Dec 10.
Mobile phone distracted driving (MPDD) is one of the most significant and common factors in distraction-affected crashes. In previous studies, MPDD has been described as a self-selected behavior that affects driving performance, rather than a multidimensionally impacted behavior. In this study, the researchers hypothesized that external environmental features significantly impacted MPDD and tested this hypothesis by structural equation modeling (SEM). Three external latent variables (road, operation, and control factors) were measured at different times during weekdays in urban areas of Texas by integrating a large number of mobile phone sensor data and roadway inventory data. A structural model was developed to test the relationship between the latent variables and the rate of drivers involved in MPDD (MPDDR) on the roadway during different time periods. Finally, the data summary and model results revealed significant temporal effects. Standardized estimates from the SEM results revealed the positive impact of roads factors in the morning peak that broader shoulders, wider medians, and smaller curve radians were correlated with higher MPDDR in the morning peak hours; the negative impact of operation factors that higher average annual daily truck traffic (truck AADT) were associated with lower MPDDR significantly. And the impact of control factors on MPDDR is positive. In other words, the road segments with a large number of traffic signals in urban areas had a higher MPDDR than those without traffic signals. These findings could assist transportation and legislation agencies in the development of appropriate countermeasures or enforcement tactics and implement them effectively to reduce the occurrence of MPDD. In addition, this study provides a novel perspective close to the actual consideration of drivers about using mobile phones while driving, in the context of MPDD research, rather than comparing driver groups and vehicle performance.
手机 distracted 驾驶 (MPDD) 是最显著和常见的分心影响碰撞因素之一。在之前的研究中，MPDD 被描述为一种自我选择的行为，会影响驾驶表现，而不是一种多维度受影响的行为。在这项研究中，研究人员假设外部环境特征对 MPDD 有显著影响，并通过结构方程建模 (SEM) 来检验这一假设。通过整合大量手机传感器数据和道路清单数据，在德克萨斯州城区的工作日不同时间测量了三个外部潜在变量（道路、操作和控制因素）。建立了一个结构模型来测试潜在变量与不同时间段内道路上驾驶员参与 MPDD（MPDDR）的比率之间的关系。最后，数据摘要和模型结果显示出显著的时间效应。SEM 结果的标准化估计揭示了道路因素在早高峰期间的积极影响，较宽的路肩、更宽的中央分隔带和较小的曲线半径与早高峰期间更高的 MPDDR 相关；操作因素的负面影响，即更高的年平均日卡车交通量 (truck AADT) 与显著较低的 MPDDR 相关。控制因素对 MPDDR 的影响是积极的。换句话说，城市地区有大量交通信号的道路段的 MPDDR 高于没有交通信号的道路段。这些发现可以帮助交通和立法机构制定适当的对策或执法策略，并有效地实施这些策略，以减少 MPDD 的发生。此外，这项研究在 MPDD 研究中提供了一个接近实际考虑驾驶员在驾驶时使用手机的新颖视角，而不是比较驾驶员群体和车辆性能。
