Variational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps

Accurate traffic flow prediction is fundamental for effective traffic management and the optimization of highway systems, particularly in complex scenarios involving highway ramps. This paper proposes a novel deep-learning method that integrates variational autoencoders (VAE) and generative adversarial networks (GAN) to enhance prediction performance and model interpretability. Traditional deep-learning approaches, such as CNNs and LSTMs, exhibit limitations in effectively capturing the inherent stochastic characteristics and spatial-temporal dependencies present in highway traffic data, especially under the influence of entry and exit ramps. To address these limitations, this research treats traffic prediction as a probabilistic distribution-fitting problem, utilizing the generative capabilities of GANs coupled with the regularizing effects of VAEs to stabilize and improve model training. The developed hybrid VAE-GAN framework was systematically evaluated using real-world data collected from the California performance measurement system (PeMS), which comprises traffic volume and speed information across multiple roadway sections. Comparisons with conventional deep-learning methods demonstrated that the VAE-GAN model achieved superior accuracy, particularly in predicting traffic conditions in scenarios involving multiple roadway segments and ramps. Specifically, results indicated that predictions leveraging multiple upstream sensor inputs significantly outperformed single-sensor predictions, and the presence of ramps notably reduced the accuracy of traditional CNN-based models. In contrast, the proposed VAE-GAN approach maintains robustness and accuracy in predictions across all tested conditions, effectively capturing the impact of ramps on traffic patterns. The findings underscore the importance of considering spatial-temporal distributions and probabilistic modeling in traffic forecasting, providing valuable insights for real-time traffic management strategies and intelligent transportation system design.