Title: Generative Traffic Simulations
Primary area: generative models
Keywords: Traffic Simulations, Traffic Optimization, Language Models for Traffic

Abstract: Controlling urban traffic is an emerging challenge for modern cities. In this work, we present a cascaded AI system that integrates a deep neural network with a large language model to generate and simulate both routine and edge-case traffic scenarios. The proposed system analyzes real-time congestion patterns and generates optimized traffic signal plans, adjusting signal timing based on vehicle flow direction and phase-level congestion to minimize delays and enhance throughput. By combining real-world traffic data with synthetically generated scenes, the approach enhances travel efficiency and minimizes wait times. To our knowledge, this is the first system that combines real-life video feeds, vehicle tracking and a large language model based code generator to create synthetic traffic scenarios and optimize traffic signal plans in SUMO .

TL;DR: This manuscript is the first system that that combines real-life video feeds, vehicle tracking and a large language model based code generator to create synthetic traffic scenarios and optimize traffic signal plans in SUMO .