How likely am I to find parking? – A practical model-based framework for predicting parking availability

Parking availability information (or occupancy of parking facility) is highly valued by travelers, and is one of the most important inputs to many parking models. This paper proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility. While the underlying queuing model can be any reasonable model, we demonstrate the framework with the well-established continuous-time Markov M\M\C\C queue in this paper. The possibility of adopting a different queuing model that can potentially incorporate the parking-searching process is also discussed. The parameter estimation module and the occupancy prediction module are both built on the underlying queuing model. To apply the estimation and prediction methods in real world, a few practical considerations are accounted for in the framework with methods to handle variations of arrival and departure patterns from day to day and within a day, including special events. The proposed framework and models are validated using both simulated and real data. Our San Francisco case studies demonstrate that the parameters estimated offline can lead to accurate predictions of parking facility occupancy both with and without real-time update. We also performed extensive numerical experiments to compare the proposed framework and methods with several pure machine-learning methods in recent literature. It is found that our approach delivers equal or better performance, but requires a computation time that is orders of magnitude less to tune and train the model. Additionally, our approach can predict for any time in the future with one training process, while pure machine-learning methods have to train a specific model for a different prediction interval to achieve the same level of accuracy.