A systematic review of generative adversarial networks for traffic state prediction: Overview, taxonomy, and future prospects

Ying Li, Fan Bai, Cheng Lyu, Xiaobo Qu, Yang Liu

Abstract: Highlights•Analysis of traffic prediction components enhances understanding of GANs.•GANs exhibit significant potential in completing missing traffic prediction data.•GAN-based models more accurately capture spatiotemporal dynamics.•Integrating multimodal data into GANs helps enhance prediction accuracy.•Crucial to develop metrics for assessing time series GANs in traffic forecasts.