Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing

The paper aims to explore the performance of short-term traffic flow prediction of the 5G (5th Generation Mobile Communication Technology) Internet of Vehicles (IoV) based on edge computing (EC) for the smart city and to further improve the intelligence of the smart city. Aiming at the current emergency of traffic congestion and road congestion, the present work adds EC to the current vehicle network, and integrates a deep convolution random forest neural network (DCRFNN). Additionally, it implements a model for short-term traffic flow prediction of a 5G vehicle network based on EC and deep learning (DL), and analyzes its performance by simulation. The results reveal that the proposed algorithm has a lower average delay cost, and the average unloading utility is stable at approximately 70%. In the prediction performance analysis, the recognition accuracy of the proposed algorithm reaches 98.06%. It is at least 1.14% higher than that of the advanced convolution neural network (CNN) algorithm proposed by other scholars and achieves a faster convergence rate. Therefore, the constructed short-term traffic prediction model implemented has a high-quality prediction performance while also ensuring a better unloading performance. The results can provide an experimental basis for traffic flow prediction and intelligent development of the smart city.