δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting

Teng Zhou, Guoqiang Han, Xuemiao Xu, Zhizhe Lin, Chu Han, Yuchang Huang, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)31-38
Number of pages8
JournalNeurocomputing
Volume247
DOIs
Publication statusPublished - 19 Jul 2017

Keywords

  • AdaBoost
  • Dynamic system
  • Short-term traffic flow forecasting
  • Stacked autoencoder
  • Time-series model

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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