The real-time crash risk analyses were proposed to establish the relationships between crash occurrence probability and pre-crash traffic operational conditions. Given its great application potentials that link with Active Traffic Management System (ATMS) for proactive safety management, it has become an important research area. Currently, researchers mainly developed the real-time crash risk analysis models with traffic flow descriptive statistics employed as explanatory variables and with re-sampled balanced dataset, which hold the limitations of insufficiently capturing the temporal-spatial traffic flow characteristics and failing to provide classification capabilities when deal with the imbalanced datasets. In this study, a Convolutional Neural Network (CNN) modelling approach with refined loss functions has been first time introduced to the real-time crash risk analyses. The primary objectives of the proposed CNN models are: (1) utilizing the tensor-based data structure to explore the multi-dimensional, temporal-spatial correlated pre-crash operational features; and (2) optimizing the loss functions to overcome the low classification accuracy issue brought by the imbalanced data. Data from the Shanghai urban expressway system were utilized for the empirical analysis. And a total of three types of loss functions, including traditional binary cross entropy, the α-weighted cross entropy and the focal loss, were introduced and being tested with varying ratios of crash and non-crash datasets. The modeling results show that the CNN model has better classification performance compared to the traditional Multi-layer Perceptrons (MLP) model with the tensor-based structure data. Besides, the developed CNN model with focal loss function has substantial classification enhancement under the imbalanced datasets. Finally, the distributions of predicting probabilities for balanced and imbalanced datasets were plotted to understand the effects of the imbalanced dataset and revealed how the proposed CNN model with focal loss function improves the model performance.

提出了实时碰撞风险分析，以建立碰撞发生概率与碰撞前交通运行状况之间的关系。鉴于其与主动交通管理系统（ATMS）关联进行主动安全管理的巨大应用潜力，它已成为重要的研究领域。当前，研究人员主要开发了实时交通事故风险分析模型，该模型以交通流描述性统计为解释变量，并重新采样了平衡数据集，这些模型存在着无法充分捕捉时空交通流特征并且无法提供分类功能的局限性。当处理不平衡的数据集时。在这个研究中，具有改进损失函数的卷积神经网络（CNN）建模方法已首次引入到实时碰撞风险分析中。所提出的CNN模型的主要目标是：（1）利用基于张量的数据结构来探索多维，时空相关的崩溃前操作特征；（2）优化损失函数，克服数据不平衡带来的分类精度低的问题。利用上海城市高速公路系统的数据进行实证分析。引入了三种损失函数，包括传统的二进制交叉熵，α加权交叉熵和焦点损失，并使用不同比率的崩溃和非崩溃数据集进行了测试。建模结果表明，与基于张量的结构数据的传统多层感知器（MLP）模型相比，CNN模型具有更好的分类性能。此外，在不平衡数据集下，具有焦点损失功能的CNN模型具有明显的分类增强。最后，绘制了平衡和不平衡数据集的预测概率分布，以了解不平衡数据集的影响，并揭示了所提出的具有焦点损失功能的CNN模型如何提高模型性能。