Abstract：
Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers\' driving behavior heterogeneity on model performance, \"traffic entropy\" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.
摘要：
识别具有高驾驶员级别风险的关键安全管理驾驶员对于改善交通安全至关重要。以前的研究通常根据汇总的统计特征评估驾驶员级别的风险(例如，驾驶暴露和驾驶行为)，这些数据是从长周期驾驶监测数据中获得的。然而,鉴于联网车辆和车载数据仪器技术的巨大进步，短期驾驶数据的收集显着增加，已成为分析的重要数据来源。在这个数据环境中,由于驾驶行为的时变特征以及不足的数据采样周期，传统上采用的聚合行为特性是不稳定的。因此，传统的基于聚合统计特征的建模方法已不再可行。而不是利用这种不可靠的统计信息来表示驾驶员级别的风险，这项研究利用驾驶行为的时间变化特征来识别短期驾驶数据环境中的关键安全管理驾驶员。具体来说,建立了驾驶行为时间变化特征与个体碰撞发生概率之间的关系。为了消除驾驶员驾驶行为异质性对模型性能的影响，提出了可以量化驾驶行为异常程度的“交通熵”指标。采用卷积神经网络（CNN）和长短期记忆（LSTM）的深度学习模型进行时间变化特征挖掘。使用从在线乘车服务获得的数据进行了实证分析。实验结果表明，基于时间变化特征的模型优于传统的基于聚合统计特征的模型。曲线下面积(AUC)指数提高4.1%。提出的流量熵指数进一步增强了5.3%的模型性能。最佳模型实现了0.754的AUC,与利用长周期驾驶数据的现有方法相当。最后,讨论了该方法在驾驶员管理程序开发中的应用及其进一步研究。
