Traffic flow prediction models - A review of deep learning techniques

Traffic flow prediction models - A review of deep learning techniques

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 tr...

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Bibliographic Details
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
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Summary: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.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2021.2010510