ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility

TL;DR

ChatSUMO is a novel LLM-based system that automates the creation and analysis of traffic simulation scenarios in SUMO, enabling users without expertise to generate customized, real-world traffic models with high accuracy.

Contribution

This paper introduces ChatSUMO, the first LLM-powered agent that automates traffic scenario generation and analysis in SUMO, integrating multi-modal inputs and user-friendly customization.

Findings

Achieved 96% accuracy in real-world Albany traffic simulation

Enabled user-friendly customization of traffic scenarios and parameters

Demonstrated effective integration of LLM with SUMO for automated analysis

Abstract

Large Language Models (LLMs), capable of handling multi-modal input and outputs such as text, voice, images, and video, are transforming the way we process information. Beyond just generating textual responses to prompts, they can integrate with different software platforms to offer comprehensive solutions across diverse applications. In this paper, we present ChatSUMO, a LLM-based agent that integrates language processing skills to generate abstract and real-world simulation scenarios in the widely-used traffic simulator - Simulation of Urban MObility (SUMO). Our methodology begins by leveraging the LLM for user input which converts to relevant keywords needed to run python scripts. These scripts are designed to convert specified regions into coordinates, fetch data from OpenStreetMap, transform it into a road network, and subsequently run SUMO simulations with the designated traffic conditions. The outputs of the simulations are then interpreted by the LLM resulting in informative comparisons and summaries. Users can continue the interaction and generate a variety of customized scenarios without prior traffic simulation expertise. For simulation generation, we created a real-world simulation for the city of Albany with an accuracy of 96\%. ChatSUMO also realizes the customizing of edge edit, traffic light optimization, and vehicle edit by users effectively.