Ranjan, Yash (University of Florida), Sengupta, Rahul (University of Florida), Rangarajan, Anand (University of Florida), Ranka, Sanjay (University of Florida)

IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle Driving at Signalized Intersections

Scheduled for presentation during the Invited Session "S19b-Artificial Transportation Systems and Simulation" (TH-EA-T19), Thursday, November 20, 2025, 14:30−14:50, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28% of fatal crashes and 58% of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: Can we create a data-driven simulator that can mimic the micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a ``live" micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-the-loop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.