CRC: Credible Residual Calibration for Traffic Signal Control under Test-Time Drift

Traffic signal control (TSC) plays a critical role in improving urban traffic efficiency. Although reinforcement learning (RL) has achieved remarkable progress in TSC, RL-based controllers are typically trained under specific traffic conditions and remain frozen during testing. In practice, environmental feedback under dynamic traffic conditions may change after a signal action is applied, leading to performance degradation. Existing solutions mainly rely on adaptation capabilities acquired before testing and remain limited in exploiting feedback changes that emerge at test time. In this paper, we introduce Action-Conditioned Feedback Distribution (ACFD) drift to characterize changes in the feedback distribution over next states and rewards conditioned on the current state and action, and propose Credible Residual Calibration CRC), a plug-in test-time adaptation framework for TSC. CRC freezes the pretrained base controller and attaches a residual calibrator to its outputs, enabling adaptation through test-time feedback. CRC uses the test-time Bellman inconsistency of the frozen base controller as an observable value-relevant signal of ACFD drift and maintains calibration-state statistics from stepwise feedback. Based on the calibration state, the residual calibrator learns action-wise candidate residual corrections, while action-wise credibility controls their release for decision making. CRC further uses calibration-state-guided action routing to adjust the decision mode and dynamic replay to update the residual calibrator from informative test-time transitions. Extensive experiments on real-world datasets and multiple base TSC controllers demonstrate that CRC consistently improves test-time control performance.