Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review

Traffic Congestion Classification Using GAN-Based Synthetic Data Augmentation and a Novel 5-Layer Convolutional Neural Network Model

Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).