Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.
J Safety Res. 2019 Dec;71:1-11. doi: 10.1016/j.jsr.2019.09.013. Epub 2019 Nov 13.
An improper driving strategy is one of the causative factors for a high probability of runoff and overturning crashes along the horizontal curves of two-lane highways. The socio-demographic and driving experience factors of a driver do influence driving strategy. Hence, this paper explored the effect of these factors on the driver's runoff risk along the horizontal curves.
The driving performance data of 48 drivers along 52 horizontal curves was recorded in a fixed-base driving simulator. The driving performance index was estimated from the weighted lateral acceleration profile of each driver along a horizontal curve. It was clustered and compared with the actual runoff events observed during the experiment. It yielded high, moderate, and low-risk clusters. Using cross-tabulation, each risk cluster was compared with the socio-demographic and experience factors. Further, generalized mixed logistic regression models were developed to predict the high-risk and high to moderate risk events.
The age and experience of drivers are the influencing factors for runoff crash. The high-risk event percentage for mid-age drivers decreases with an increase in driving experience. For younger drivers, it increases initially but decreases afterwards. The generalized mixed logistic regression models identified young drivers with mid and high experience and mid-age drivers with low-experience as the high-risk groups.
The proposed index parameter is effective in identifying the risk associated with horizontal curves. Driver training program focusing on the horizontal curve negotiation skills and graduated driver licensing could help the high-risk groups. Practical applications: The proposed index parameter can evaluate driving behavior at the horizontal curves. Driving behavior of high-risk groups could be considered in highway geometric design. Motor-vehicle agencies, advanced driver assistance systems manufacturers, and insurance agencies can use proposed index parameter to identify the high-risk drivers for their perusal.
在双车道公路的水平曲线路段，不当的驾驶策略是导致车辆侧滑和翻车事故概率较高的原因之一。驾驶员的社会人口统计学和驾驶经验因素确实会影响驾驶策略。因此，本文探讨了这些因素对驾驶员在水平曲线路段侧滑风险的影响。
在固定基础驾驶模拟器中记录了 48 名驾驶员在 52 个水平曲线上的驾驶性能数据。驾驶性能指标是根据每个驾驶员在水平曲线上的加权横向加速度曲线估算得出的。将其聚类并与实验中观察到的实际侧滑事件进行比较，得出高、中、低风险聚类。使用交叉表比较每个风险聚类与社会人口统计学和经验因素。进一步，建立广义混合逻辑回归模型以预测高风险和高到中风险事件。
驾驶员的年龄和经验是导致侧滑事故的影响因素。中年驾驶员的高风险事件百分比随着驾驶经验的增加而降低。对于年轻驾驶员，它最初会增加，但随后会减少。广义混合逻辑回归模型确定了具有中高经验的年轻驾驶员和经验较低的中年驾驶员为高风险群体。
提出的指标参数可有效识别与水平曲线相关的风险。针对水平曲线谈判技巧和分级驾照的驾驶员培训计划可以帮助高风险群体。
提出的指标参数可用于评估水平曲线处的驾驶行为。可以考虑在公路几何设计中考虑高风险群体的驾驶行为。机动车机构、先进驾驶员辅助系统制造商和保险公司可以使用建议的指标参数来识别高风险驾驶员。
