Intelligent Transportation System for Sustainable and Efficient Urban Mobility Traffic Flow Prediction Using Optimized Deep Learning Approach

Traffic blockage is described as the transport system's condition, characterized by the automobiles' low speed. It is also because of roads, climatic conditions, particular zones, temperature, the number of events on that day etc. It mainly happens in metropolitan cities. Due to the development of communication technology, most organizations have transferred their conventional methods to computing methods. The primary objective of this study is to investigate deep learning algorithms that can efficiently capture the inherent relationships present in network traffic flows, thereby facilitating precise predictions. To evaluate the precision and predictive potential of these approaches, two initial baseline models are constructed utilizing Recurrent Neural Network (RNN) models. These baseline models make predictions for target values' mean and mode, drawing from the training data. A model is considered accurate and proficient if it surpasses the basic, naive benchmarks established based on predefined performance metrics. The optimization process, relying on Red Fox (RF), is determined through a quantitative analysis employing well-established evaluation criteria commonly used in supervised deep learning, including accuracy, precision, recall, Mean Squared Error (MSE), and Root Mean Square Error (RMSE).