Traffic Anomaly Detection through Generative Modeling of Multi-Agent Interactions in Traffic Flow

In intelligent transportation systems, effectively modeling and interpreting the complex interactions among autonomous agents, such as vehicles and pedestrians, is crucial for traffic management and safety. This paper introduces a novel generative traffic flow model that employs a generative pretrained Transformer to capture multiagent interactions and detect anomalies in traffic patterns. We introduce a two-stage tokenization process for set-structured traffic data, efficiently encoding variable-sized agent states into fixed-length sequences suitable for generative modeling. We demonstrate that anomalies, which often indicate potential hazards or non-compliant behaviors, can be identified as deviations from learned normal interaction patterns among agents through a zero-shot detection mechanism. Our experimental results in simulated urban settings highlight the model's capability to detect various types of traffic anomalies with high accuracy. This work significantly advances agent-based traffic modeling and underscores its potential for enhancing traffic safety and efficiency in multi-agent systems.