Ariel University, School of Health Sciences, the Occupational Therapy Department, Ariel, Israel; Tel Aviv University, Sackler Faculty of Medicine, School of Health Professions, Department of Occupational Therapy, Tel Aviv, Israel.
Tel Aviv University, Sackler Faculty of Medicine, School of Health Professions, Department of Occupational Therapy, Tel Aviv, Israel.
J Safety Res. 2022 Sep;82:402-408. doi: 10.1016/j.jsr.2022.07.007. Epub 2022 Jul 22.
Due to the relative rarity of crashes, researchers use traffic offenses, police records, public complaints, and In-Vehicle Data Recorder (IVDR) data as proxies for assessing crash risk. In this study, a unique IVDR system, called Vision-Based Technology [(VBT), (Mobileye Inc.)] was used to monitor perilous naturalistic driving events, such as insufficient distance from other vehicles and pedestrian or bicycle rider near-misses. The study aimed to test the convergent validity of VBT as an indicator of crash involvement risk.
Data from 61 professional drivers working for a large bus company were analyzed (16 of 77 in the original data cohort were excluded for insufficient VBT data). Data included: recorded VBT data, objective data collected from official records (crash records provided by the bus company, and public complaints of reckless driving), self-report data regarding crash involvement, and police tickets. The correlation between VBT, objective and self-reported data was analyzed. Binary-logistic regression modeling (BLM) was used to calculate the odds ratio (OR) for participants involved in a car crash.
Correlations were found between the total VBT risk score and official crash records, public complaints, and self-reports of crash involvement. The BLM correctly classified 90% of those who were involved in a crash (sensitivity) and 60% of those who were "crash-free" (specificity). The VBT total risk score was the only significant contributing factor to crash risk, and for each point of increase, the odds of being involved in a crash increased by a factor of 1.55.
It is the first study to provide empirical evidence validating the VBT as an indicator of crash involvement and driver safety among professional bus drivers.
VBT technology can provide researchers and clinicians a better understanding of bus drivers' risky driving behaviors- a valuable contribution to road safety interventions for this target group.
由于碰撞事故相对较少，研究人员通常使用交通违法行为、警方记录、公众投诉和车载数据记录仪 (IVDR) 数据来评估碰撞风险。在本研究中，使用了一种独特的 IVDR 系统，称为基于视觉的技术 [(VBT)，(Mobileye Inc.)] 来监测危险的自然驾驶事件，例如与其他车辆和行人和/或自行车骑手的距离不足以及险些发生碰撞。本研究旨在测试 VBT 作为碰撞事故风险指标的收敛有效性。
对一家大型巴士公司的 61 名专业驾驶员的数据进行了分析（由于 VBT 数据不足，原始数据队列中有 16 人被排除在外）。数据包括：记录的 VBT 数据、从官方记录中收集的客观数据（巴士公司提供的碰撞记录和鲁莽驾驶的公众投诉）、关于碰撞事故参与的自我报告数据，以及警方罚单。分析了 VBT、客观和自我报告数据之间的相关性。使用二元逻辑回归建模 (BLM) 计算参与者发生车祸的几率比 (OR)。
VBT 总风险评分与官方碰撞记录、公众投诉和自我报告的碰撞事故参与度之间存在相关性。BLM 正确分类了 90%的参与者（敏感性）和 60%的未发生碰撞的参与者（特异性）。VBT 总风险评分是唯一显著的碰撞风险因素，每增加一分，发生碰撞的几率就会增加 1.55 倍。
这是第一项提供实证证据的研究，证明 VBT 可作为专业巴士司机碰撞事故参与度和驾驶员安全性的指标。
VBT 技术可以为研究人员和临床医生提供更好地了解巴士司机危险驾驶行为的机会——这是针对该目标群体进行道路安全干预的宝贵贡献。
