Early Detection of Freeway Traffic Anomalies With a Cycle-Consistent Bidirectional Generative Adversarial Network

Rapidly identifying anomalous events on freeways can significantly improve emergency response time and reduce congestion. However, the unpredictable nature of these incidents, along with the limitations of current monitoring systems, makes accurate and timely detection challenging. In this study, we propose a Cycle-Consistent Bidirectional Graph Generative Adversarial Network (CCB-GraphGAN) model to enhance lane-level traffic anomaly detection on freeways. By treating freeway traffic information as graph-structured data, our model captures the joint distribution of normal traffic patterns and their underlying latent representations through an adversarial process. It also incorporates cycle-consistency constraints to effectively reconstruct both normal traffic data and its latent features during training. During the inference phase, we employ an autoencoder-based anomaly detection approach to identify lane-level traffic anomalies. We experimentally evaluate the performance of our method through comparative and case study analyses using the recently released Freeway Traffic Anomalous Event Detection (FT-AED) dataset. The results indicate that our method outperforms all baseline models in detecting freeway anomalous events early. Notably, it achieves a lower percentage of missed crashes compared to baseline models. With a false positive rate (FPR) of only 1%, our method reduces detection delay by identifying crashes an average of 5 minutes earlier than their official report time. Additionally, an analysis of real-world crash events further confirms our method’s effectiveness in accurately identifying freeway anomalies at an early stage.