Review the Technique for Road Traffic Flow Prediction Using Hybrid Deep Learning

Abstract

A very important part of the Intelligent Transportation System is a process that predicts how traffic will move. Because of the rise in traffic congestion in cities, more time is spent waiting at road crossings, more fuel is wasted, and there are more pollutants in the air. In this study, we want to improve an existing deep hybrid model that can predict short-term traffic congestion by learning the spatiotemporal features in a flexible way. It's possible that the model in question will pick up on each of these traits one by one. The GRU and the application of the convolution layer both use the residual learning method as their main way to teach. Both of these ways are good for getting the information that needs to be taken into account that depends on where it is and when it is. Both of these ways do a good job of capturing these dependencies. The results of the experiment show that the strategy given can make accurate predictions even when traffic is difficult.