OracleTSC Stabilizes LLM-Based Traffic Control with Reward Hurdle

The introduction of OracleTSC represents a significant stride in the application of large language models (LLMs) to critical infrastructure, specifically traffic signal control (TSC). This innovation directly tackles the inherent instability of reinforcement learning-based TSC systems, which traditionally suffer from sparse and delayed feedback loops. By integrating LLMs, OracleTSC aims to provide transparent, natural language reasoning, a crucial factor for public trust in autonomous decision-making systems. The core challenge addressed is the difficulty LLMs face in reinforcement finetuning for TSC due to marginal changes in congestion metrics from most actions.

OracleTSC stabilizes LLM-based TSC through two primary mechanisms. First, a reward hurdle mechanism filters out weak learning signals by subtracting a calibrated threshold from environmental rewards, ensuring the model focuses on meaningful feedback. Second, uncertainty regularization maximizes the probability of selected responses, promoting consistent decisions across sampled outputs. These mechanisms collectively enable a compact LLaMA3-8B model to achieve substantial performance improvements on the LibSignal benchmark, including a 75% reduction in travel time and a 67% decrease in queue length compared to pretrained baselines. Crucially, the system preserves interpretability through natural language explanations, a key differentiator from traditional black-box reinforcement learning solutions. The demonstrated cross-intersection generalization, with 17% lower travel time and 39% lower queue length on structurally different intersections without additional finetuning, highlights its potential for broad applicability.

The implications for urban planning and smart city initiatives are profound. OracleTSC offers a pathway to more efficient, adaptable, and publicly acceptable traffic management systems. The ability to deploy LLMs for real-time control with enhanced stability and interpretability could accelerate the adoption of AI in other complex public services. However, successful real-world deployment will necessitate rigorous testing in diverse urban environments, addressing edge cases, and ensuring fail-safe mechanisms are in place to manage system anomalies or unexpected traffic conditions. The balance between autonomous decision-making and human oversight will be a critical consideration for public policy and regulatory frameworks.