# Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

arXiv: https://arxiv.org/abs/2408.00948 · HTML: https://mlsanity.com/paper/2408.00948/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [MTD-GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections](https://mlsanity.com/paper/2307.16118/) (2023)
- [TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models](https://mlsanity.com/paper/2403.02221/) (2024)
- [Enhancing Network Management Using Code Generated by Large Language Models](https://mlsanity.com/paper/2308.06261/) (2023)
- [Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems](https://mlsanity.com/paper/2406.06865/) (2024)
- [Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events](https://mlsanity.com/paper/2406.13894/) (2024)
- [Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics](https://mlsanity.com/paper/2406.09438/) (2024)
- [Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition](https://mlsanity.com/paper/2406.10712/) (2024)
- [Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language](https://mlsanity.com/paper/2406.08519/) (2024)
- [Transformer Models in Education: Summarizing Science Textbooks with AraBART, MT5, AraT5, and mBART](https://mlsanity.com/paper/2406.07692/) (2024)
- [Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques](https://mlsanity.com/paper/2406.08520/) (2024)
- [The Use of Multimodal Large Language Models to Detect Objects from Thermal Images: Transportation Applications](https://mlsanity.com/paper/2406.13898/) (2024)

## Built on by (most cited first, 6 of 6)

- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [HazardNet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices](https://mlsanity.com/paper/2502.20572/) (2025)
- [Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends](https://mlsanity.com/paper/2504.16134/) (2025)
- [SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification](https://mlsanity.com/paper/2511.14977/) (2025)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)
- [On the security and privacy of LLMs in Mobility](https://mlsanity.com/paper/2609.26295/) (2026)

## Abstract (arXiv)

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management.