  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.
