This study proposes a stability index to quantitatively evaluate the psychological stability of drivers during truck platooning. Truck platooning is a technique in which a manually driven lead truck is followed by automated trucks using V2X communication and onboard sensors. While significant technical advances have been made in truck platooning, research on drivers’ psychological comfort remains limited. Due to the shorter intervehicle time gaps compared to conventional trucking, truck platooning raises concerns regarding drivers’ psychological stability. The proposed index quantifies this aspect and is validated using a driving simulator. Both the stability index calculations and the survey consistently indicate that when the time gap decreases to 0.6 s (approximately 15 m) or less, drivers’ psychological stability deteriorates. However, increasing the time gap beyond 0.6 s does not significantly improve it. Furthermore, this study investigates the effect of the see-through functionality, which provides real-time front-view footage of the preceding vehicle to the following driver. The results confirm that this functionality enhances psychological stability, even under a short time gap. The stability index should serve as a practical indicator for designing truck platooning systems that consider drivers’ psychological stability and may be extended to various advanced driving technologies, including general vehicle platooning.

Urban pothole management conventionally relies on reactive, complaint-driven maintenance, which inevitably suffers from operational delays and inefficient resource distribution. To catalyze a paradigm shift toward proactive maintenance, this study proposes a comprehensive data-driven framework that integrates machine learning, explainable AI (XAI), and Geographic Information System (GIS) spatial clustering to predict localized pothole risks. Utilizing a dataset of 21,397 spatial points (13,104 occurrences and 8293 non-occurrences) collected from Gwangju Metropolitan City (2022–2024), 19 diverse spatial, environmental, and geometric variables were extracted. To ensure strict statistical rigor and mitigate data leakage, dual-track encoding and Phi-K correlation analyses were applied to capture complex non-linear dependencies. Among the six predictive models evaluated, the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 90.47% and an ROC-AUC of 0.957. Subsequent XAI interpretation using Shapley Additive exPlanations (SHAP) empirically revealed that physical scale and spatial vulnerabilities synergistically accelerate pavement fatigue. Furthermore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) successfully translated these predictive probabilities into 14 highly localized hotspots. Ultimately, the findings of this study provide a highly actionable, evidence-based guideline for local governments to abandon uniform budget distribution and transition to a preemptive, concentrated allocation of municipal resources directed exclusively at high-risk zones.