SignalClaw synthesizes interpretable, composable traffic signal control skills through LLM-guided evolution that matches top baselines on routine SUMO scenarios and outperforms them on emergency and transit events while remaining editable by engineers.
LLMLight: Large language models as traffic signal control agents
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5verdicts
UNVERDICTED 5representative citing papers
OverFlowLight is a real-time traffic signal framework that detects queue overflows via multi-modal sensing and inserts dedicated phases in a hybrid RL setup, reducing overflows by 60.4% and increasing throughput by 18.2% across 43 deployed intersections.
C2T learns an LLM-derived common-sense reward function to improve cooperative multi-intersection traffic control policies, outperforming standard MARL baselines on efficiency, safety, and energy proxies while allowing prompt-based policy tuning.
ARMove is a transferable framework for human mobility prediction that combines agentic LLM reasoning, feature management, and large-small model synergy to outperform baselines on several metrics while improving interpretability and robustness.
A survey synthesizing LLM and MM-LLM uses in transportation operations, mobility services, and decision support while noting challenges like data heterogeneity and real-time needs.
citing papers explorer
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SignalClaw: LLM-Guided Evolutionary Synthesis of Interpretable Traffic Signal Control Skills
SignalClaw synthesizes interpretable, composable traffic signal control skills through LLM-guided evolution that matches top baselines on routine SUMO scenarios and outperforms them on emergency and transit events while remaining editable by engineers.
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OverFlowLight: Real-Time Gridlock Prevention and Traffic Signal Optimization for Urban Intersections
OverFlowLight is a real-time traffic signal framework that detects queue overflows via multi-modal sensing and inserts dedicated phases in a hybrid RL setup, reducing overflows by 60.4% and increasing throughput by 18.2% across 43 deployed intersections.
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C$^2$T: Captioning-Structure and LLM-Aligned Common-Sense Reward Learning for Traffic--Vehicle Coordination
C2T learns an LLM-derived common-sense reward function to improve cooperative multi-intersection traffic control policies, outperforming standard MARL baselines on efficiency, safety, and energy proxies while allowing prompt-based policy tuning.
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ARMove: Learning to Predict Human Mobility through Agentic Reasoning
ARMove is a transferable framework for human mobility prediction that combines agentic LLM reasoning, feature management, and large-small model synergy to outperform baselines on several metrics while improving interpretability and robustness.
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Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
A survey synthesizing LLM and MM-LLM uses in transportation operations, mobility services, and decision support while noting challenges like data heterogeneity and real-time needs.