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

arXiv: https://arxiv.org/abs/2409.09040 · HTML: https://mlsanity.com/paper/2409.09040/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 25 of 25)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Language-Guided Traffic Simulation via Scene-Level Diffusion](https://mlsanity.com/paper/2306.06344/) (2023)
- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [ADAPT: Action-aware Driving Caption Transformer](https://mlsanity.com/paper/2302.00673/) (2023)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation Safety](https://mlsanity.com/paper/2307.15311/) (2023)
- [A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices](https://mlsanity.com/paper/2001.00269/) (2020)
- [BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning](https://mlsanity.com/paper/2310.10357/) (2023)
- [Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management](https://mlsanity.com/paper/2405.03076/) (2024)
- [An Overview of Agent-based Traffic Simulators](https://mlsanity.com/paper/2102.07505/) (2021)
- [GenFollower: Enhancing Car-Following Prediction with Large Language Models](https://mlsanity.com/paper/2407.05611/) (2024)
- [Multi-range Reasoning for Machine Comprehension](https://mlsanity.com/paper/1803.09074/) (2018)

## Built on by (most cited first, 30 of 34)

- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Challenger: Affordable Adversarial Driving Video Generation](https://mlsanity.com/paper/2505.15880/) (2025)
- [Realistic Corner Case Generation for Autonomous Vehicles with Multimodal Large Language Model](https://mlsanity.com/paper/2412.00243/) (2024)
- [A Lightweight Large Language Model-Based Multi-Agent System for 2D Frame Structural Analysis](https://mlsanity.com/paper/2510.05414/) (2025)
- [From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2502.02145/) (2025)
- [Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review](https://mlsanity.com/paper/2506.06301/) (2025)
- [CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis](https://mlsanity.com/paper/2505.07853/) (2025)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring](https://mlsanity.com/paper/2502.11304/) (2025)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2508.14527/) (2025)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Beyond Words: Evaluating Large Language Models in Transportation Planning](https://mlsanity.com/paper/2409.14516/) (2024)
- [SUMO-MCP: Leveraging the Model Context Protocol for Autonomous Traffic Simulation and Optimization](https://mlsanity.com/paper/2506.03548/) (2025)
- [AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Models](https://mlsanity.com/paper/2511.06804/) (2025)
- [TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control](https://mlsanity.com/paper/2512.20996/) (2025)
- [LinguaSim: Interactive Multi-Vehicle Testing Scenario Generation via Natural Language Instruction Based on Large Language Models](https://mlsanity.com/paper/2510.08046/) (2025)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [Chat2SPaT: A Large Language Model Based Tool for Automating Traffic Signal Control Plan Management](https://mlsanity.com/paper/2507.05283/) (2025)
- [GTA: Generative Traffic Agents for Simulating Realistic Mobility Behavior](https://mlsanity.com/paper/2601.16778/) (2026)
- [Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving](https://mlsanity.com/paper/2607.14387/) (2026)
- [SimSkill: A Self-Evolving LLM Agent for Skill and Knowledge Accumulation in Traffic Simulation](https://mlsanity.com/paper/2609.03753/) (2026)
- [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026)
- [Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems](https://mlsanity.com/paper/2502.05425/) (2025)
- [Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis](https://mlsanity.com/paper/2507.10382/) (2025)
- [GraphTrafficGPT: Enhancing Traffic Management Through Graph-Based AI Agent Coordination](https://mlsanity.com/paper/2507.13511/) (2025)
- [LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support](https://mlsanity.com/paper/2604.23902/) (2026)
- [What Will Happen Next: Large Models-Driven Deduction for Emergency Instances](https://mlsanity.com/paper/2605.08599/) (2026)
- [Decoupled Intelligence: A Multi-Agent LLM Framework for Controllable Traffic Scenario Generation in SUMO](https://mlsanity.com/paper/2605.27685/) (2026)

## Abstract (arXiv)

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.