Data-Driven Optimization of Taxi Parking Spaces for Strategic Demand Alignment

Optimizing taxi parking spaces in urban environments is crucial for reducing customer wait times and enhancing operational efficiency. Traditional approaches, based on static statistical methods and relying on Points of Interest (PoIs), often fail to address dynamic demand patterns, particularly with the rise of app-based ride-hailing services. This paper introduces a data-driven framework combining automatic segmentation and association rule mining techniques to dynamically align taxi parking spaces with demand zones. Using DBSCAN for parking cluster identification and the Apriori algorithm for correlating these clusters with high-demand areas, our methodology demonstrates a practical solution. Experimental results reveal positive alignment between identified parking spaces and manually validated demand areas, highlighting the reliability of our approach in modern urban transportation systems.