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: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
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).