Small sample sizes and imbalanced datasets have been two difficulties in previous traffic incident detection-related studies. Moreover, real-time characteristics of incident detection models must be improved to satisfy the needs of traffic management. In this study, a hybrid model is proposed to address the above problems. In the proposed model, a generative adversarial network (GAN) is used to expand the sample size and balance datasets, and a temporal and spatially stacked autoencoder (TSSAE) is used to extract temporal and spatial correlations of traffic flow and detect incidents. Using a real-world dataset, the model is evaluated from different aspects. The results show that the proposed model, considering both temporal and spatial variables, outperforms some benchmark models. The model can both increase the incident sample size and balance the dataset. Furthermore, the sample selection method improves the real-time capacity of the detection.

小样本量和不平衡的数据集一直是之前交通事故检测相关研究中的两个难点。此外，必须改进事件检测模型的实时特性以满足交通管理的需要。在这项研究中，提出了一种混合模型来解决上述问题。在所提出的模型中，生成对抗网络（GAN）用于扩展样本量和平衡数据集，时间和空间堆叠自动编码器（TSSAE）用于提取交通流的时间和空间相关性并检测事件。使用真实世界的数据集，从不同方面评估模型。结果表明，所提出的模型同时考虑了时间和空间变量，优于一些基准模型。该模型既可以增加事件样本量，又可以平衡数据集。此外，样本选择方法提高了检测的实时性。