Optimizing Parking Efficiency Using Parking Duration Prediction: A Case Study of a Shopping Mall Parking Facility

This is the authors' abstract. We don't add key points for this paper.

Parking space availability in densely populated areas has been declining for decades, while extending existing facilities is often not feasible. This paper proposes assigning vehicles to parking spots based on their predicted duration of stay as a means to increase the customer satisfaction of an existing facility. Real-world parking data from an Austrian shopping mall is analyzed to identify vehicle and contextual features that correlate with parking duration, and both regression and classification models are trained to estimate a visitor’s likely stay time upon arrival. A discrete-event simulation then compares four assignment strategies against an unguided baseline, covering a simple nearest-spot approach, a machine learning-based strategy, and a perfect-knowledge upper bound. The primary efficiency gain stems from the elimination of driver search time, which any structured assignment achieves regardless of prediction quality. Duration-aware placement provides a smaller, additional benefit at higher occupancy levels by reserving spaces near the entrance for short-stay visitors, and the gap between the classifier-based strategy and the perfect-knowledge bound remains moderate, confirming that even imperfect predictions yield a meaningful share of the theoretically achievable improvement. It must be noted, however, that the predictive accuracy of the models remains limited: regression errors are near 60 min and classifier accuracy is only modestly above chance, reflecting the fundamental difficulty of inferring individual visitor intent from observable vehicle and arrival features alone. This limitation constrains the practical applicability of duration-aware assignment and should be considered carefully in any real-world deployment decision.