Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76159
Title: delta-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting
Authors: Zhou, T
Han, GQ
Xu, XM
Lin, ZZ
Han, C
Huang, YC
Qin, J 
Keywords: Short-term traffic flow forecasting
AdaBoost
Stacked autoencoder
Time-series model
Dynamic system
Issue Date: 2017
Publisher: Elsevier
Source: Neurocomputing, 2017, v. 247, p. 31-38 How to cite?
Journal: Neurocomputing 
Abstract: Accurate and timely traffic flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Recently, the stacked autoencoder has been proven promising for traffic flow forecasting but still exists some drawbacks in certain conditions. In this paper, a training samples replication strategy is introduced to train a series of stacked autoencoders and an adaptive boosting scheme is proposed to ensemble the trained stacked autoencoders to improve the accuracy of traffic flow forecasting. Furthermore, sufficient experiments have been conducted to demonstrate the superior performance of the proposal.
URI: http://hdl.handle.net/10397/76159
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2017.03.049
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