EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Algorithms
🤖 AI Summary
Conventional fixed-time traffic signal control relies heavily on manual formulae (e.g., Webster’s method) and empirical parameter tuning, exhibiting poor adaptability and limited effectiveness in heterogeneous congestion scenarios.
Method: This paper proposes a large language model (LLM)-driven programming agent framework that uniquely integrates LLMs, program synthesis, and evolutionary search for autonomous design of traffic signal control algorithms. It represents policies as executable Python functions and iteratively optimizes them via closed-loop simulation feedback—requiring no human intervention.
Contribution/Results: The framework automatically discovers novel, interpretable, and deployable control algorithms. In single-intersection experiments, the synthesized algorithm reduces average vehicle delay by 20.1% and stops per vehicle by 47.1% compared to Webster’s method, demonstrating substantial performance gains and yielding structured, actionable insights into optimization mechanisms.
Application Category
📝 Abstract
In traffic engineering, the fixed-time traffic signal control remains widely used for its low cost, stability, and interpretability. However, its design depends on hand-crafted formulas (e.g., Webster) and manual re-timing by engineers to adapt to demand changes, which is labor-intensive and often yields suboptimal results under heterogeneous or congested conditions. This paper introduces the EvolveSignal, a large language models (LLMs) powered coding agent to automatically discover new traffic signal control algorithms. We formulate the problem as program synthesis, where candidate algorithms are represented as Python functions with fixed input-output structures, and iteratively optimized through external evaluations (e.g., a traffic simulator) and evolutionary search. Experiments on a signalized intersection demonstrate that the discovered algorithms outperform Webster's baseline, reducing average delay by 20.1% and average stops by 47.1%. Beyond performance, ablation and incremental analyses reveal that EvolveSignal modifications-such as adjusting cycle length bounds, incorporating right-turn demand, and rescaling green allocations-can offer practically meaningful insights for traffic engineers. This work opens a new research direction by leveraging AI for algorithm design in traffic signal control, bridging program synthesis with transportation engineering.
Problem
Research questions and friction points this paper is trying to address.
Automating traffic signal control algorithm discovery using LLMs
Overcoming labor-intensive manual design and suboptimal performance
Generating interpretable and high-performing algorithms via program synthesis
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-powered coding agent for traffic algorithms
Evolutionary search with external traffic simulator evaluations
Python program synthesis with fixed input-output structure
🔎 Similar Papers
No similar papers found.
