A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model

Traffic prediction on the road, as a vital part of the Intelligent Transportation System (ITS) has attracted much attention recently. It is always one of the hot topics about how to implement an efficient, robust, and accurate vehicular traffic prediction system. With the help of Machine Learning-based (ML) methods, especially Deep Learning-based (DL) methods, the accuracy of the prediction model is increased. However, we also noticed that there are still many open challenges under ML-based vehicular traffic prediction model real-world implementation. Firstly, the time consumption for training DL model is relatively large when compared to parametric models, such as ARIMA, SARIMA. Second, it is still a hot topic for road traffic prediction that how to capture the spacial relationship between road detectors, which is affected by the geographic correlation, as well as the time change. The last but not the least, it is important for us to implement the prediction system into the real world; meanwhile, we should find a way to make use of the advanced technology applied in ITS to improve the prediction system itself. In this paper, we focus on improving the features of the prediction model, which can be helpful for implementing the model in the real world. We present a new hybrid deep learning model by using Graph Convolutional Network (GCN) and the deep aggregation structure (i.e., the sequence to sequence structure) of Gated Recurrent Unit (GRU). Meanwhile, in order to solve the real-world prediction problem, i.e., the online prediction task, we present a new online prediction strategy by using refinement learning. In order to further improve the model's accuracy and efficiency when applied to ITS, we make use of an efficient parallel training strategy while taking advantage of the vehicular cloud structure. (C) 2020 Elsevier B.V. All rights reserved.