A crash occurrence risk prediction model based on variational autoencoder and generative adversarial network
Wenqi Zhu, Chaofeng Lü, Xiqun Chen
Abstract
Traffic crash risk prediction is pivotal for proactive traffic safety management but faces challenges due to the extreme imbalance between crash and non-crash data. This paper proposes integrating variational autoencoder into a generative adversarial network (VAE-GAN) for crash data augmentation without information loss. VAE-GAN generates higher-quality data due to its superior deep generative capabilities. For the crash occurrence risk prediction task, we utilize a convolutional neural network (CNN) trained on the balanced datasets generated by VAE-GAN. Two kinds of category determinations are tested for better prediction results by using prediction probability maximum and selecting threshold. The prediction model integrating variational autoencoder into a deep convolutional generative adversarial network (VAE-DCGAN) exhibits the best performance. Furthermore, we leverage Shapley additive explanations (SHAP) to interpret the key features and patterns impacting prediction results. This analysis gains insights into the underlying mechanisms of crash occurrences, and helps improve proactive traffic management and control strategies.