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 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.

Automated summary

Traffic flow prediction is crucial for intelligent transport systems to mitigate congestion and improve safety and efficiency. Traditional shallow machine learning models are unable to handle the exponential growth in vehicle numbers, leading to an increased focus on deep learning models. This paper reviews the latest deep learning models for traffic flow prediction, discussing the impact of various factors and identifying the best models for different scenarios.