Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks

The purpose of this paper is the development of data science models for the detection of empty on-street parking spaces in urban road networks based on data provided by in-vehicle cameras that are already, or soon will be, a standard vehicle equipment. A rolling spatial interval is used to identify the existence of an on-street parking space and the properties of empty spaces are used to determine the availability of the parking space. Convolutional neural networks are developed, trained, and evaluated with the use of images from a moving vehicle camera. The images are preprocessed and converted to suitable matrices, so that only the useful information for the empty on-street parking space detection problem is preserved. The optimized convolutional networks, in terms of structural and learning parameters, provided predictions for the detection of empty on-street parking spaces with approximately 90% average accuracy. The proposed model performs better than the relatively complex SVMs, which supports its appropriateness as an approach. Finally, the implementation of a framework, which integrates the developed models to produce meaningful parking information for drivers in real time, is discussed.