Data Engineering for Horizon-Aware Smart Parking Systems

Abstract Accurate short-term parking availability forecasting is an important component of intelligent transportation systems. While numerous machine learning and deep learning models have been proposed, most approaches rely on a single predictor and implicitly assume uniform performance across forecast horizons, despite evidence that accuracy varies with lead time. This paper presents a systematic evaluation of multi-step (1–24 h) parking availability forecasting using sensor-based occupancy data from two urban parking facilities. We show that different model classes exhibit complementary strengths at different horizons, with no single model consistently achieving the lowest error across the full forecast range. To address this limitation, we propose a horizon-aware hybrid forecasting approach that combines two complementary base models using validation-driven weighted blending. Two variants are considered: a Global Weighted Blend with a shared weight and a Local Weighted Blend with horizon-specific weights. Experimental results show that both hybrid approaches outperform individual baseline models, with the Local Weighted Blend achieving the best overall performance across all evaluation metrics. These findings demonstrate that horizon-aware blending provides a simple and effective strategy for improving multi-step parking availability forecasts. As an outlook, we propose a context-aware, adaptive model-management architecture for sensor-based smart city systems.