Traffic flow prediction models–A review of deep learning techniques

Anirudh Ameya Kashyap, Shravan Raviraj, Ananya Devarakonda, Shamanth R. Nayak K, K. V. Santhosh, Soumya J. Bhat

Research output: Contribution to journalReview articlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Article number2010510
JournalCogent Engineering
Issue number1
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemical Engineering(all)
  • Engineering(all)


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