Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24061, USA.
Real-Time Remote Sensing, LLC, Salem, VA 24153, USA.
Accid Anal Prev. 2021 Sep;159:106267. doi: 10.1016/j.aap.2021.106267. Epub 2021 Jun 26.
Speeding behaviors are quite common and are known to affect the risk and severity outcomes of vehicular crashes. Naturalistic driving data allows for the direct observation of speeding and the development of evidence-based causal structures for this behavior. This limits biases associated with self-reported speeding prevalence, allowing for more precise speeding measures than post-crash investigations and for the evaluation of driver attributes associated with speeding across a wide variety of locations and road types. Data from the Second Strategic Highway Research Program Naturalistic Driving Study were used to identify speeding events that were aggregated based on their duration and degree of speeding above the posted speed limit. The events were summarized as a likelihood of speeding metric for each trip in the dataset. These likelihood of speeding measurements were also aggregated across drivers and compared, using a beta binomial regression model, to driver questionnaire answers that addressed drivers' perception of their speeding as well as different driver-specific factors that are suspected of having an influence on speeding behaviors. Results showed that, consistent with past studies, age and gender significantly influenced the likelihood of speeding. For age, the odds of speeding exhibited a significant downward trend across increasing age groups; 16-24 year olds exhibited odds of speeding that were 1.5 times the odds of drivers that were 80 or more years old. For gender, males exhibited larger odds of speeding than females (1.1 times larger). In addition, the odds of speeding were larger at lower posted speed limits. The odds of speeding in 10-20 mph zones were 9.5 times the odds of zones with speed limits greater than 60 mph, implying that drivers may be unaware of the risks associated with speeding in low speed limit areas. Several questionnaire answers related to drivers' perception of speeding were also predictive of the likelihood of speeding.
超速行为相当普遍，已知会影响车辆碰撞的风险和严重程度结果。自然驾驶数据可直接观察超速行为，并为这种行为建立基于证据的因果结构。这限制了与自我报告的超速流行率相关的偏差，从而可以比事故后调查更精确地测量超速，并评估与各种地点和道路类型的超速相关的驾驶员属性。使用第二战略公路研究计划自然驾驶研究的数据来识别超速事件，这些事件根据超速持续时间和超过限速的程度进行汇总。这些事件被汇总为数据集中每次行程的超速可能性度量。这些超速可能性测量值也根据驾驶员进行汇总，并使用贝塔二项式回归模型与驾驶员问卷回答进行比较，这些问卷回答涉及驾驶员对自己超速的看法以及驾驶员特定因素，这些因素被怀疑对超速行为有影响。结果表明，与过去的研究一致，年龄和性别对超速的可能性有显著影响。对于年龄，超速的几率随着年龄组的增加呈显著下降趋势；16-24 岁的驾驶员超速的几率是 80 岁或以上驾驶员的 1.5 倍。对于性别，男性超速的几率大于女性（大 1.1 倍）。此外，在较低的限速下，超速的几率更大。限速为 10-20 英里/小时的区域的超速几率是限速大于 60 英里/小时的区域的超速几率的 9.5 倍，这表明驾驶员可能没有意识到在低限速区域超速的风险。与驾驶员对超速的看法相关的几个问卷回答也可预测超速的可能性。
