An Integrated Approach for the Near Real-Time Parking Occupancy Prediction

In a city, the usage optimization of parking spaces with a near real-time response to car drivers can significantly reduce the unnecessary cruising for parking and the additional congestion of regional traffic. As the foundation to achieve such an optimization, a parking occupancy prediction method is required to address the emerging challenges of training a simple but effective model. To fill the gap, this paper proposes a novel approach that enables the integration of Time Series Decomposition (TSD), Gated Recurrent Unit (GRU), and First-order Model-agnostic Meta-learning (FOMAML) for feature engineering, model building, and model pre-training, respectively. Moreover, as shown by a detailed evaluation, such an integration strengthens the proposed approach, named Meta TSD-GRU, which outperforms other state-of-the-art methods with 1) prediction errors reduced by about 45% on average, 2) the speed of model adaptation and convergence improved about 2 and 102 times against the methods with and without pre-training, respectively, and 3) the generalizability of the model enhanced to handle various time intervals of forecasting and types of parking lots under a consistent and stable performance.