Optimal deep neural network based road traffic management system for Internet of Things based smart city environment

Internet of Things (IoT) solutions in smart transportation infrastructure deliver a transformative method to enhance operational efficacy and productivity while optimising performance across all scopes. Using IoT abilities, transportation networks might be observed in real-time, permitting data-driven decision making and enhanced connectivity, finally decreasing road congestion and increasing safety integration. A solution of intelligent transportation can improve the traffic flow in urban cities by observing traffic patterns and altering traffic signal times. The goal is to determine and help supportable methods of transport, to increase an Intelligent Transportation System (ITS) that utilises real-time data to improve safety, reduce congestion, and enhance green applications. ITS influences new and evolving technologies to create mobility that is more satisfying and economical in smart cities. At present, deep learning is an effective method to discover hidden visions into ITS without being programmed explicitly by learning from data. In this manuscript, a Hybrid Feature Selection and Deep Neural Network for Decision Support Systems in Road Traffic Management (HFSDNN-DSSRTM) model is proposed for smart IoT-integrated cities. The HFSDNN-DSSRTM model aims to design a decision support system for efficient road traffic management in IoT-enabled smart cities using advanced methodologies. Primarily, the data pre-processing stage is employed at dual levels, such as missing values handling and normalisation methods. For an effective feature selection, the HFSDNN-DSSRTM model employs filter, wrapper, and embedded methods to identify and keep only the most valuable features that contribute to the classification task. Finally, the temporal convolutional network with attention mechanism (TCN-AM) method is used for classification. The comparison analysis of the HFSDNN-DSSRTM approach portrayed a superior accuracy value of 98.75% over existing methods.