Parking occupancy prediction and analysis - a comprehensive study

The seemingly interminable search for an available parking spot wastes time and fuel while polluting the environment. Yet, the inefficiency of the process persists, due to a lack of real-time and near-future information. Researchers are addressing this issue by developing systems that combine various modeling techniques with historical and real-time data to predict parking occupancy. The objective of this study was to provide a comprehensive review of the existing academic research on this topic by conducting a thorough examination of 108 published research papers, journals, and articles. The findings revealed that hybrid models that combine different approaches are most effective in making accurate predictions, as they capture a wider range of patterns and relationships in parking occupancy dynamics. Neural network models, particularly convolutional and recurrent neural networks, demonstrate exceptional capabilities in processing intricate spatial and temporal patterns. Deep learning models, particularly long short-term memory models, excel in handling large and complex datasets and show potential for predicting short-term parking availability. Time series models, such as autoregressive integrated moving average models, are deemed suitable for capturing temporal patterns. The findings of the study will serve as a valuable resource for researchers who can build upon the existing knowledge and make more informed decisions when developing parking prediction models, ultimately contributing to the advancement of the field.