The rapid growth of motor vehicles in urban areas has led to an increasing demand for parking facilities and requires tariff policies that are more adaptive to real field conditions. This study aims to develop a parking revenue potential classification model to support the formulation of progressive parking tariff policies using a data-driven and spatial analysis approach. The dataset includes vehicle attributes, parking volume, and temporal parking usage patterns. Parking revenue potential is categorized into low, medium, and high classes using a quantile-based approach. Unlike most previous studies that focus on parking occupancy prediction, this study proposes a revenue-oriented spatiotemporal classification model integrating spatial coordinates and temporal parking patterns to support adaptive tariff policy formulation. The Random Forest algorithm is applied to classify parking revenue potential into low, medium, and high categories, achieving an accuracy of 90.24% for two-wheeled vehicles and 89.02% for four-wheeled vehicles. The classification results are integrated into an interactive Streamlit-based dashboard that visualizes the spatial distribution of parking revenue potential and enables simulations of progressive tariff adjustments based on spatial zones and temporal conditions. The proposed system functions as a decision support tool for parking management, aiming to improve operational efficiency and sustainably increase regional parking retribution revenue.