A Data-driven Approach for Probabilistic Traffic Prediction and Simulation at Signalized Intersections

Intersections are conflict zones where the paths of vehicles and pedestrians intersect. They are particularly prone to accidents, with a significant portion of both fatal and nonfatal crashes occurring at these locations. Understanding the diverse behaviors of traffic participants at intersections is crucial for mitigating risks and improving safety. However, conducting experiments in real-world settings to study such behaviors is impractical and hazardous. In this paper, we propose a datadriven approach for predicting and simulating vehicular and pedestrian traffic at signalized intersections. Our method focuses on accurately modeling their behavior and interactions at a busy intersection. We introduce a multi-agent trajectory prediction model, based on the Conditional Variational Autoencoder (CVAE) framework, to generate plausible future trajectories. By incorporating intersection geometry, agent history, and signal state, our model produces a realistic prediction of traffic dynamics. Our model outperforms a strong baseline model-Trajectron++ [1]-by 17% in terms of final displacement errors. We then apply a rule-based trajectory sampling approach to simulate long-horizon behaviors, enabling the proactive identification of potential risks and the exploration of counterfactual scenarios. This research contributes to the development of predictive traffic analytics systems, facilitating safer and more efficient intersection management strategies.