arXiv Machine Learning October 8, 2026 By Leizhen Wang, Peibo Duan, Zhenlin Qin, Yancheng Ling, Jian Xu, Yue Wang, Hao Wang, Zhenliang Ma

EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs

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EvoSignal is an LLM‑guided evolutionary framework that treats traffic signal control as a modular program design problem, separating traffic feature extraction, local phase prioritization, and optional network‑based priority adjustment. Starting from established strategies, it iteratively improves programs using congestion and signal operation feedback, ultimately producing control rules that operate without online LLM inference. In simulations on two real‑world road networks, the best EvoSignal program reduced waiting time by 16.8–49.2% compared to 20 conventional, reinforcement‑learning, and LLM‑based baselines, and a travel‑time/queue‑length prioritizing program consistently outperformed all baselines across multiple scenarios.

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