A Summative Analysis of Deep Neural Networks in Traffic Flow Prediction

Abstract

In the modern society, traffic congestion is a major problem that calls for the creation of intelligent transportation systems (ITS), of which precise traffic forecasting is an essential element. The use of deep learning architectures to the short-term traffic forecasting problem will be summarized in this study. Prior to thoroughly analyzing important deep learning models, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and their hybrid variants, it first addresses the drawbacks of conventional statistical and machine-learning techniques. The analysis culminates in the discussion of Spatiotemporal Graph Neural Networks (STGNNs), arguing that deep learning, particularly through graph-based architectures capable of modeling complex non-Euclidean spatial relationships and temporal dynamics, has become the indispensable framework for accurate traffic forecasting. In its conclusion, the article discusses existing issues including computing demands and data quality and makes recommendations for future research, such as long-term prediction and dynamic graph learning.