Data-Driven Traffic Simulation for an Intersection in a Metropolis

TL;DR

This paper introduces a data-driven traffic simulation environment for metropolitan intersections that leverages real-world data and advanced trajectory forecasting models to improve accuracy and realism in traffic modeling.

Contribution

It presents a novel simulation framework combining real-world data with state-of-the-art trajectory forecasting, enabling more accurate and controllable traffic simulations.

Findings

TrajNet++ achieved 0.36 FDE in experiments.

Simulation runs at 20 FPS on NVIDIA A100 GPU.

Model configurations demonstrate flexible traffic modeling.

Abstract

We present a novel data-driven simulation environment for modeling traffic in metropolitan street intersections. Using real-world tracking data collected over an extended period of time, we train trajectory forecasting models to learn agent interactions and environmental constraints that are difficult to capture conventionally. Trajectories of new agents are first coarsely generated by sampling from the spatial and temporal generative distributions, then refined using state-of-the-art trajectory forecasting models. The simulation can run either autonomously, or under explicit human control conditioned on the generative distributions. We present the experiments for a variety of model configurations. Under an iterative prediction scheme, the way-point-supervised TrajNet++ model obtained 0.36 Final Displacement Error (FDE) in 20 FPS on an NVIDIA A100 GPU.