Understanding Behavioral Uncertainty in Parking Reservation Systems: A Hybrid SEM-Logit-Machine Learning Approach

Parking reservation systems (PRS) are promoted as smart urban parking tools, yet their continued use remains limited because users face both technological uncertainty and schedule-related uncertainty. This study develops a behavioral analysis framework that combines structural equation modeling (SEM), a stated-preference binary logit model, and Random Forest learning. SEM examines how perceived usefulness, perceived ease of use, perceived risk, social influence, and behavioral attitude shape intention to reuse. The binary logit model examines whether users retain their reserved lot under 10 reservation mechanisms and three arrival scenarios. Random Forest is then used to test nonlinear prediction and interaction effects, with intention to reuse measured as the average of the two reuse-intention items and model performance evaluated by the conventional coefficient of determination (R2), mean squared error, and mean absolute error. The results show that perceived risk suppresses perceived usefulness and behavioral attitude, early and especially late arrival sharply reduce reservation retention, and discount intensity is the strongest positive operational lever. Random Forest additionally shows that the effect of perceived risk depends on perceived ease of use: a more intuitive interface buffers the negative effect of risk on predicted reuse intention. These findings indicate that behavioral uncertainty in PRS is simultaneously perceptual, situational, and interactive. PRS design should therefore combine flexible time management, transparent real-time information, and low-friction user interfaces.