Traffic flow prediction models - A review of deep learning techniques

• Published in: Cogent engineering Vol. 9; no. 1
• Main Authors: Kashyap, Anirudh Ameya, Raviraj, Shravan, Devarakonda, Ananya, Nayak K, Shamanth R, K V, Santhosh, Bhat, Soumya J
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Artificial neural networks; Coders; Deep learning; deep neural networks; Feature extraction; hybrid models; intelligent transport system; Intelligent transportation systems; Machine learning; Neural networks; Prediction models; Recurrent neural networks; Traffic congestion; Traffic flow; traffic flow prediction; Traffic models; unsupervised learning
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2021.2010510

Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.