Traffic-Alpha/LLM-Assisted-Light

This repository contains the code for the paper "LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments".

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This project helps traffic managers optimize signal timings in complex urban environments, especially when dealing with congestion or sensor malfunctions. It takes real-time traffic data, such as vehicle occupancy and intersection layouts, and uses a large language model to suggest human-like signal control strategies. The output is a series of explainable traffic signal adjustments. This tool is for traffic operations engineers and urban planners responsible for city-wide traffic flow.

No commits in the last 6 months.

Use this if you need an intelligent system to manage traffic lights that can adapt to unexpected events and explain its decisions.

Not ideal if you are looking for a simple, pre-set traffic light schedule without dynamic adaptation or complex decision-making capabilities.

traffic-management urban-planning smart-cities transportation-operations congestion-control
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 13 / 25

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99

Forks

11

Language

Python

License

Apache-2.0

Last pushed

Jul 10, 2025

Commits (30d)

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