In addition to enhancing traffic safety and facilitating prompt emergency response, traffic incident detection plays an indispensable role in intelligent transportation systems by providing real-time traffic status information. This enables the realization of intelligent traffic control and management. Previous research has identified that apart from employing advanced algorithmic models, the effectiveness of detection is also significantly influenced by challenges related to acquiring large datasets and addressing dataset imbalances. A hybrid model combining transformer and generative adversarial networks (GANs) is proposed to address these challenges. Experiments are conducted on four real datasets to validate the superiority of the transformer in traffic incident detection. Additionally, GANs are utilized to expand the dataset and achieve a balanced ratio of 1:4, 2:3, and 1:1. The proposed model is evaluated against the baseline model. The results demonstrate that the proposed model enhances the dataset size, balances the dataset, and improves the performance of traffic incident detection in various aspects.

翻译：除提升交通安全和促进紧急响应外，交通事件检测通过提供实时交通状态信息，在智能交通系统中发挥着不可或缺的作用，从而助力实现智能交通控制与管理。已有研究表明，除采用先进算法模型外，检测效果还显著受限于大规模数据集获取及数据集不平衡问题。针对上述挑战，本文提出一种融合Transformer与生成对抗网络（GANs）的混合模型。通过在四个真实数据集上开展实验，验证了Transformer在交通事件检测中的优越性。同时，采用GANs扩增数据集，实现1:4、2:3和1:1的平衡比例。将所提模型与基线模型进行对比评估，结果表明该模型不仅扩充了数据集规模并实现数据平衡，还从多个维度提升了交通事件检测性能。