[论文解读] Extracting useful information from Basic Safety Message Data: An empirical study of driving volatility measures and crash frequency at intersections

Mohsen Kamrani, Ramin Arvin+1 more|arXiv (Cornell University)|Mar 6, 2018

Traffic and Road Safety被引用 5

一句话总结

本研究提出利用高频基本安全消息（BSM）数据推导驾驶波动性指标——如速度和加速度偏离正常值的程度——作为交叉路口碰撞风险的替代指标。基于经验证的密歇根州安全驾驶员计划数据，采用泊松回归模型分析发现，更高的波动性，尤其是超出±2σ阈值及增加的随机波动性，显著预示着碰撞频率上升，从而可在高风险地点实现主动安全干预。

ABSTRACT

With the emergence of high-frequency connected and automated vehicle data, analysts have become able to extract useful information from them. To this end, the concept of is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles' instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. The intersection data was error-checked and verified for accuracy. Then, for each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities, i.e., hot-spots where crashes are waiting to happen. Therefore, a deeper analysis of these intersections can be undertaken and proactive safety countermeasures considered at high volatility locations to enhance safety.

研究动机与目标

• 利用交叉路口的高频联网车辆数据开发并验证新的替代性安全指标。
• 识别与碰撞频率增加相关的驾驶波动性指标——基于速度、加速度和急动（jerk）的指标。
• 将这些波动性指标与通行量及几何特征数据结合，以改进碰撞预测模型。
• 通过识别因驾驶行为异常而可能发生碰撞的“热点区域”，实现主动安全规划。

提出的方法

• 收集并验证了来自密歇根州安全驾驶员计划部署的高频基本安全消息（BSM）数据。
• 利用交叉路口处的瞬时速度、加速度和急动数据，计算了37种不同的波动性指标。
• 使用均值±2个标准差定义阈值区间，以识别显著偏离正常驾驶模式的数据点。
• 应用固定参数与随机参数泊松回归模型，估计碰撞频率与波动性、通行量及交叉路口几何特征之间的关系。
• 使用经过误差检查和验证的交叉路口清单及碰撞数据，以确保模型准确性。
• 通过量化车辆通过交叉路口过程中的时变随机波动性，评估动态驾驶行为。

实验结果

研究问题

• RQ1 从BSM数据中推导出的哪些驾驶波动性指标与更高的交叉路口碰撞频率关联最强？
• RQ2 与正常驾驶行为的偏离程度（速度和加速度超出±2σ）与碰撞发生之间存在何种相关性？
• RQ3 车辆速度的时变随机波动性幅度在多大程度上可预测碰撞频率？
• RQ4 当与通行量和几何特征结合时，波动性指标能否作为交叉路口安全风险的可靠替代指标？
• RQ5 这些波动性指标是否有助于在事故发生前识别高风险交叉路口？

主要发现

• 在控制通行量和几何因素后，三种特定驾驶波动性指标的增加仍与更高的交叉路口碰撞频率正相关。
• 在速度和加速度方面，超出±2个标准差区间的数据点比例越高，碰撞频率显著增加。
• 车辆通过交叉路口过程中，速度的时变随机波动性越大，碰撞率也越高。
• 这些波动性指标能够有效识别出碰撞更可能发生的位置，即‘热点区域’，从而支持主动安全干预。
• 研究结果表明，高频BSM数据可为交通管理部门提供可操作的、数据驱动的安全洞察。
• 本研究证实，驾驶波动性，尤其是速度和加速度方面的波动性，可作为交叉路口碰撞风险的稳健代理指标。