Abstract: High Occupancy Vehicle (HOV) and High Occupancy Tolling (HOT) lanes have been commonly practiced in several jurisdictions to reduce traffic congestion and promote car pooling. Camera-based methods have been recently proposed for a cost-efficient, safe and effective HOV/HOT lane enforcement with the prevalence of video cameras in transportation imaging applications. An important step in automated lane enforcement systems is classification of localized window/windshield images to distinguish passenger from no-passenger vehicles to identify violators. This can be performed using deep convolutional neural networks (CNNs), which are shown to significantly outperform hand-crafted features in several classification tasks. Training/fine-tuning CNNs require a set of passenger/no-passenger images manually labeled by an operator, which requires substantial time and effort that can result in excessive operational cost and overhead. In this paper, we study adaptability of three popular CNNs (i.e., AlexNet, VGG-M, and GoogLeNet) across different domains in classifying passenger/no-passenger images as part of an automated HOV/HOT lane enforcement system. Our experiments over 40, 000 side-view vehicle images show many interesting insights for domain adaptability of these deep learning architectures.