A self-adaptive multi-view framework for multi-source information service in cloud ITS

Shan Xue, Li Xiong*, Shufen Yang, Lu Zhao

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In traditional intelligent transportation system (ITS), the information source is often collected from a single view. However, as ITS becoming increasingly complicated, there is a need to describe one object from several different information views/domains. Information from multiple sources can provide extra worthwhile information for ITS users especially in cloud environment. Moreover, existing local ITSs usually provide information by fixed algorithms, which is not adequate to the dynamic transportation scenarios that produce big traffic data with time series. In this paper, we propose a complete self-adaptive multi-view framework for multi-source information service in cloud ITS, which mainly consists of a Newton multi-parameter optimization, a multi-layer feed-forward neural network and a finite multi-view mixture distribution. A simulation on real-world application, with six different types of information views, demonstrates the underlying effectiveness of the proposed framework.

Original languageEnglish
Pages (from-to)205-220
Number of pages16
JournalJournal of Ambient Intelligence and Humanized Computing
Volume7
Issue number2
DOIs
Publication statusPublished - 1 Apr 2016
Externally publishedYes

Keywords

  • Cloud computing
  • Intelligent transportation system
  • ITS
  • Multi-layer feed-forward neural network
  • Multi-source information
  • Self-adaption

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