Integration of VCNN models with RVFLwoDL to boost the parking space classification

Parking space classification is critical in elevating traffic congestion, reducing air pol- lution, and enhancing drivers’ convenience. This study introduces a robust model for parking space classification, ingeniously combining variants of the Convolutional Neu- ral Network (VCNN) with the Incremental Random Vector Function Link without di- rect link (I-RVFLwoDL). The primary innovation consists of substituting the fully connected layer of the VCNN with I-RVFLwoDL. This eliminates the need for a costly backpropagation procedure, resulting in a substantial decrease in training time. The integration of VCNN with I-RVFLwoDL utilizes the I-RVFLwoDL’s rapid learning efficency and robust generalization capabilities. I-RVFLwoDL simplifies the network structure by eliminating the complex neuron pathways typically found in other established methodologies. The proposed hybrid model’s effectiveness is rigorously as- sessed utilizing three established datasets: PKLot, CNRPark, and CNRPark+EXT. The system’s ability to distinguish between occupied and vacant parking spaces is demon- strated through various performance metrics used in machine learning. The proposed model’s performance is also evaluated against existing deep learning models to illustrate its superiority. This research presents significant potential for intelligent transportation systems, providing an efficient solution for parking space classification.