Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems

A cyber-communication infrastructure is required for physical components like control systems, sensors, actuators, and the environment to communicate and collaborate in real-time effectively. This is what cyber-physical systems (CPSs) provide. Deep Learning (DL) techniques have sped up and improved the progress of Intelligent Transportation Systems (ITS) in recent years, especially in problem domains traditionally addressed with analytical or statistical solutions. In addition to advancing driverless vehicle development to a new level, the improvements brought about by DL applications have led to better traffic organization and scheduling, increased security and safety on transit roads, reduced maintenance costs, and optimized performance for public transportation ride-sharing companies. The primary goal of this work is to offer a complete study and awareness of the uses of DL models on ITS, as well as to demonstrate the development in ITS research that has been owing to DL studies. This article briefly introduces the reader to various DL methods before thoroughly examining and describing how these methods are now being used in the transportation sector. Deep learning models are trained on this real-world traffic information to detect better and forecast the probability of crashes. This work aims to do just that by providing a multi-perspective assessment of deep learning-based techniques for traffic forecasting. This paper offers a summary and taxonomy of current traffic forecast systems. We provide a compilation of the most cutting-edge methods currently used for traffic forecasting. In addition, we assess the efficacy of various approaches using a public, real-world dataset and provide an evaluation and analysis of our findings. These findings demonstrate that, related to state-of-the-art shallow models, a deep model is superior at traffic detection and achieves equivalent results in terms of traffic prediction. We suggest a new deep learning framework, an Attention-based hybrid Convolutional Neural network with Long Short Term Memory (LSTM) (AHCNLS), to perform real-time traffic prediction from a data mining approach to enhance driver and passenger safety. The suggested approach considers the spatial and temporal connection between GPS trajectories and contextual elements. Using a publicly available dataset, our proposed technique is evaluated and shown to have advantages over competing methods.