Jointly predicting secondary crash occurrence and spatiotemporal location via generative adversarial networks under...

Secondary crashes, occurring within the spatiotemporal impact area of primary crashes, tend to exacerbate delays and contribute to further injuries. Existing studies typically model the occurrence or spatiotemporal location (i.e., time gap and distance gap from the primary crash) of secondary crashes separately. However, severe data imbalance remains a major challenge, as the limited availability of secondary crash samples restricts the model's ability to learn minority patterns and generalize effectively. Although data augmentation techniques have been increasingly explored to alleviate this issue, existing approaches often struggle to capture the complex dependencies between dynamic (e.g., traffic flow) and static (e.g., road conditions) features. To address these challenges, we propose VarFusiGAN-Transformer, a hybrid framework that integrates generative modeling and predictive learning for joint prediction of secondary crash occurrence and spatiotemporal location. The proposed VarFusiGAN model employs Long Short-Term Memory (LSTM) networks to enhance the generation of multivariate long-sequence data, while incorporating a static data generator and an auxiliary discriminator to better learn the joint distribution of dynamic and static features. The prediction module performs multi-task joint prediction of both the occurrence and spatiotemporal location of secondary crashes. The proposed framework is evaluated using real-world traffic datasets. To comprehensively assess the quality of generated data, multiple criteria are adopted, including statistical distribution consistency, joint distribution similarity, and inter-variable correlation preservation between real and synthetic data. Experimental results demonstrate that the VarFusiGAN-balanced data significantly enhance classification performance (e.g., G-mean, F1-score, AUC-PR) and spatiotemporal prediction accuracy (e.g., MAE, RMSE). Compared with baseline methods, the proposed framework achieves superior performance in both data generation quality and prediction accuracy, providing an effective solution for secondary crash risk assessment and proactive traffic safety management.