Many studies have tried to use the surrogate safety measures (SSM) estimated from the microscopic traffic simulations. However, it is difficult to adopt these developed SSM to reflect real-world traffic conditions when the developed network in the simulation is not calibrated and validated accordingly. This paper proposed a method to develop the pattern-based surrogate safety measure (PSSM) using individual vehicle trajectory data. The PSSM can be estimated based on the pattern of hazardous driving behaviour (HDB). Using digital tacho graph data collected from the commercial vehicles, HDB patterns were obtained. Various PSSMs were developed and validated with the observed crash data using Random Forest. Then, the surrogate safety performance function was estimated based on the frequency of HDB. To enhance model performance, machine learning and data mining techniques were applied. The results show that sudden deceleration, sudden lane change, sudden overtaking and sudden U-turn are related to traffic crashes during HDB. The results also show that high potential for safety improvement was identified in the road section linking the urban and suburban areas. The findings from this study can provide new approach to adopt real-time individual vehicle trajectory data to evaluate safety performance of network levels.

中文翻译：

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使用车辆数据作为公路事故数据的替代品

许多研究试图使用从微观交通模拟中估计的替代安全措施 (SSM)。然而，当模拟中开发的网络没有进行相应的校准和验证时，很难采用这些开发的 SSM 来反映现实世界的交通状况。本文提出了一种使用单个车辆轨迹数据开发基于模式的替代安全措施 (PSSM) 的方法。PSSM 可以根据危险驾驶行为 (HDB) 的模式进行估计。使用从商用车辆收集的数字转速图数据，获得了 HDB 模式。使用随机森林通过观察到的碰撞数据开发和验证了各种 PSSM。然后，基于HDB的频率估计替代安全性能函数。为了提高模型性能，应用了机器学习和数据挖掘技术。结果表明，HDB期间突然减速、突然变道、突然超车和突然掉头与交通事故有关。结果还表明，在连接市区和郊区的路段中发现了提高安全性的巨大潜力。这项研究的结果可以为采用实时单个车辆轨迹数据评估网络级别的安全性能提供新的方法。