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IJMLC 2021 Vol.11(2): 143-151 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1027

An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

Feng Yu, Jinglong Fang, Bin Chen, and Yanli Shao

Abstract—Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

Index Terms—Traffic flow prediction, convolutional neural network, spatio-temporal features processing, incremental learning, active learning.

The authors are with the School of Computer Science, Hangzhou Dianzi University, Hangzhou, China (e-mail: hdu_yufeng@163.com, fjl@hdu.edu.cn, hertz158123@gmail.com, shaoyanli@hdu.edu.cn).

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Cite: Feng Yu, Jinglong Fang, Bin Chen, and Yanli Shao, "An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 143-151, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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