Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, TN, 37996, USA.
Urban Design 4 Health, 24 Jackie Circle East Rochester, NY, 14612, USA.
Accid Anal Prev. 2021 Mar;151:105873. doi: 10.1016/j.aap.2020.105873. Epub 2020 Dec 21.
Driving errors and violations are identified as contributing factors in most crash events. To examine the role of human factors and improve crash investigations, a systematic taxonomy of driver errors and violations (TDEV) is developed. The TDEV classifies driver errors and violations based on their occurrence during the theoretically based perception-reaction process and analyzes their contributions in safety critical events. To empirically explore errors and violations, made by drivers of instrumented vehicles, in diverse built environments, this study harnesses unique and highly detailed pre-crash sensor data collected in the Naturalistic Driving Study (NDS), containing 673 crashes, 1,331 near-crashes and 7,589 baselines (no-event). Human factors are categorized into recognition errors, decision errors, performance errors, and errors due to the drivers' physical condition or their lack of contextual experience/familiarity, and intentional violations. In the NDS data, built environments (measured by roadway localities) are classified based on roadway functional classification and land uses, e.g., residential areas, school zones, and church zones. Based on the crash percentage to baseline percentage in a specific locality, interstates and open country/open residential (rural and semi-rural settings) may pose lower risks, while urban, business/industrial, and school zone locations showed higher crash risk. Human errors and violations by instrumented vehicle drivers contributed to 93% of the observed crashes, while roadway factors contributed to 17%, vehicle factors contributed in 1%, and 4% of crashes contained unknown factors. The most common human errors were recognition and decision errors, which occurred in 39% and 34% of crashes, respectively. These two error types occurred more frequently (each contributing to nearly 39% of crashes) in business or industrial land use environments (but not in dense urban localities). The findings of this study reveal continued prevalence of human factors in crashes. The distribution of driving errors and violations across different roadway environments can aid in the implementation of driver assistance systems and place-based interventions that can potentially reduce these driving errors and violations.
驾驶失误和违规被认为是大多数事故发生的因素。为了研究人为因素的作用并改进事故调查，开发了一种驾驶员失误和违规行为的系统分类法（TDEV）。TDEV 根据理论上基于感知-反应过程中出现的情况对驾驶员失误和违规行为进行分类，并分析它们在安全关键事件中的贡献。为了在不同的建成环境中实证研究驾驶员的失误和违规行为，本研究利用自然驾驶研究（NDS）中收集的独特且详细的碰撞前传感器数据，该研究包含 673 起事故、1331 起接近事故和 7589 起基线（无事故）。人为因素分为识别错误、决策错误、操作错误以及驾驶员身体状况或缺乏环境经验/熟悉度引起的错误和故意违规。在 NDS 数据中，根据道路功能分类和土地利用，将建成环境（通过道路位置测量）分类，例如住宅区、学校区和教堂区。基于特定位置的事故百分比与基线百分比，州际公路和开阔乡村/开阔住宅区（农村和半农村地区）的风险可能较低，而城市、商业/工业和学校区的位置显示出更高的事故风险。安装了仪器的车辆驾驶员的人为失误和违规行为导致了 93%的观察到的事故，而道路因素导致了 17%，车辆因素导致了 1%，4%的事故包含未知因素。最常见的人为失误是识别和决策失误，分别发生在 39%和 34%的事故中。这两种错误类型在商业或工业土地利用环境中更为常见（但在密集的城市地区则不然），分别导致近 39%的事故发生。本研究的结果表明，人为因素在事故中仍然普遍存在。不同道路环境中驾驶失误和违规行为的分布有助于实施驾驶员辅助系统和基于位置的干预措施，从而有可能减少这些驾驶失误和违规行为。
