The era of ‘Big Data’ provides opportunities for researchers to have deep insights into traffic safety. By taking advantages of ‘Big Data’, this study proposes a data-driven method to develop a Copula-Bayesian Network (Copula-BN) using a large-scale naturalistic driving dataset with multiple features. The Copula-BN is able to explain the causality of a risky driving maneuver. As compared with conventional BNs, the Copula-BN developed in this study has the following advantages: the Copula-BN 1. Has a more rational and explainable structure; 2. Is less likely to be over-fitting and can attain more satisfactory prediction performance; and 3. Can handle not only discrete but also continuous features. In terms of technical innovations, Shapley Additive Explanation (SHAP) is used for feature selection, while Gaussian Copula function is employed to build the dependency structure of the Copula-BN. As for applications, the Copula-BNs are used to investigate the causality of risky lane-changing (LC) and car-following (CF) maneuvers, upon which the comparisons are made between the two essential but risky driving maneuvers. In this study, the Copula-BNs are developed based on the Second Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database. Upon network evaluation, the Copula-BNs for both risky LC and CF maneuvers demonstrate satisfactory structure performance and promising prediction performance. Feature inferences are conducted based on the Copula-BNs to respectively illustrate the causation of the two risky maneuvers. Several interesting findings related to features’ contribution are discussed in this paper. To a certain extent, the Copula-BN developed using the data-driven method makes a trade-off between prediction and causality within the ‘Big Data’. The comparison between risky LC and CF maneuvers also provides a valuable reference for crash risk evaluation, road safety policy-making, etc. In the future, the achievements of this study could be applied in Advanced Driver-Assistance System (ADAS) and accident diagnosis system to enhance road traffic safety.

“大数据”时代为研究人员提供了深入了解交通安全的机会。通过利用“大数据”的优势，本研究提出了一种数据驱动的方法，该方法使用具有多个特征的大规模自然驾驶数据集来开发Copula-贝叶斯网络（Copula-BN）。Copula-BN能够解释危险驾驶操作的因果关系。与传统的BN相比，本研究开发的Copula-BN具有以下优点：Copula-BN 1.具有更合理和可解释的结构；2.不太可能过度拟合，并且可以获得更令人满意的预测性能；3.不仅可以处理离散特征，还可以处理连续特征。在技​​术创新方面，使用了Shapley附加说明（SHAP）进行功能选择，而高斯Copula函数则用于构建Copula-BN的依存结构。至于应用，Copula-BN用于调查危险车道变更（LC）和跟车（CF）机动的因果关系，在此基础上，对两种基本但有风险的驾驶机动进行了比较。在这项研究中，Copula-BN是根据第二高速公路研究计划（SHRP2）自然驾驶研究（NDS）数据库开发的。通过网络评估，Copula-BN用于危险的LC和CF演习均显示出令人满意的结构性能和有希望的预测性能。基于Copula-BN进行特征推断，以分别说明这两种风险机动的因果关系。本文讨论了一些与特征贡献有关的有趣发现。在某种程度上，使用数据驱动方法开发的Copula-BN在“大数据”中进行了预测和因果关系之间的权衡。有风险的LC和CF操作之间的比较也为碰撞风险评估，道路安全政策制定等提供了有价值的参考。将来，这项研究的成果可以应用于高级驾驶员辅助系统（ADAS）和事故诊断中系统，以提高道路交通安全。