A Novel Deep Learning Approach of BH & SH on MANET basis Traffic Prediction with Big Data

Abstract

Intelligent transportation systems can't function well without reliable data on current traffic conditions. As the volume of traffic data has increased in the last several years, we've entered the era of big data in transportation. Current methods for estimating traffic flow have been let down many real-world applications, which depend mostly on shallow traffic prediction models. Therefore, we'll review the difficulty of predicting traffic flow using detailed architectural models and a vast quantity of actual traffic data. Methods for traffic flow forecasting that consider both spatial and temporal correlations are given in this article. A layered auto encoder model is used to teach traffic flow characteristics since it builds up greedy layer by layer. Deep architecture models for traffic flow prediction have never before used auto encoders as building blocks. Researchers have also shown that the recommended method for estimating traffic flow is more accurate.