Exploring and Evaluating Deep Learning Techniques for Traffic Prediction in Urban Environments

Abstract

Traffic congestion has been getting worse as a result of the growing population in urban areas that rely on various forms of transportation. However, transportation infrastructure has made significant strides over the last several decades. Traffic prediction plays an important part of intelligent transportation systems in smart cities, promoting relief on traffic congestion. The purpose of this paper is to survey and evaluate deep learning-based traffic forecasting techniques in urban areas. It aims to give a wholesome understanding of how these methods can be used in traffic management and control. The review touches on different methods and mechanisms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer attention model, graph neural networks or hybrid models such diffusion convolutional recurrent neural networks, spatial-temporal graph neural networks, spatiotemporal graph convolutional networks, and attention-based spatiotemporal graph convolutional networks. This paper also discusses potential real-time applications in traffic prediction, congestion management, and road safety. This is also accompanied with insights on bottlenecks such as data quality, computational constraints, and the need for real-time processing. There are two most effective networks that can handle spatial information—time-series traffic features and complex operations—which are CNNs for short-term prediction and RNNs for long term in the view of deep learning. Their use is particularly evident in smart city initiatives and intelligent transportation systems. Finally, deep learning holds significant promise in this field; however, it needs to overcome these challenges for successful traffic prediction.