Reinforcement Learning for Congestion Mitigation in Inland Freight Terminals: A Simulation-Based Serial Mediation Analysis of Operational Learning Stability and Logistics Efficiency

This study explains how reinforcement learning (RL) contributes to congestion mitigation in inland freight terminal operations by testing a serial process model in which RL strengthens operational learning stability (OLStab), OLStab improves logistics efficiency, and logistics efficiency lowers congestion. Rather than presenting RL as a stand-alone congestion-reduction instrument, the paper examines a distinct inland-terminal application in which congestion emerges from interacting gate, yard, transfer, and dispatch frictions. Using a simulation-based explanatory design calibrated to a realistic macro-logistics context, and interpreting the results as simulation-informed evidence rather than direct field proof, the study analyzes 500 episode-level observations representing complete terminal runs under varying control conditions. The results show that RL positively affects OLStab, OLStab positively affects logistics efficiency, and logistics efficiency negatively affects congestion. The serial indirect pathway from RL through OLStab and logistics efficiency to congestion is statistically significant, whereas the direct effect of RL on congestion becomes non-significant once the mediators are introduced. Decision latency sensitivity does not significantly moderate the RL-to-OLStab relationship, suggesting that latency-related boundary conditions are more context-specific than the main capability pathway. The article contributes by offering a cautious simulation-based and mechanism-centered explanation of RL-enabled congestion mitigation in inland terminals, by treating OLStab as a simulation-grounded intermediate operational stability index, and by showing that the empirical pattern is better explained by theory-ordered simulator-level mechanism than by a residual direct RL effect.