MC-TES: An efficient mobile phone based context-aware traffic state estimation framework

Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka

Research output: Contribution to journalArticle

Abstract

This paper proposes a notable mobile phone based context-aware traffic state estimation (MC-TES) framework whereby the essential issues of low and uncertain penetration rate are thoroughly resolved. A novel intelligent context-aware velocity-density inference circuit (ICIC) and a practical artificial neural network (ANN) based prediction approach are proposed. The ICIC model not only improves the traffic state estimation effectiveness but also minimizes the critical penetration rate required in the mobile phone based traffic state estimation (M-TES). The ANN- based prediction approach is considered as a complement of the ICIC in cases of an unacceptably low or unknown penetration rate. In addition, the difficulty in selecting the "right" traffic state estimation model, namely among the ICIC and the ANN, under the condition of an uncertain penetration rate is resolved. The experimental evaluations confirm the effectiveness, the feasibility as well as the robustness of the proposed approaches. As a result, this research contributes to accelerating the realization of mobile phone-based intelligent transportation systems (M-ITSs) or of the M-TES systems in specific.

Original languageEnglish
Pages (from-to)76-89
Number of pages14
JournalJournal of Information Processing
Volume21
Issue number1
DOIs
Publication statusPublished - 2013

Fingerprint

State estimation
Mobile phones
Networks (circuits)
Neural networks

Keywords

  • Inference circuit
  • Mobile context-aware
  • Mobile phone based ITS
  • Penetration rate
  • Traffic state estimation

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

MC-TES : An efficient mobile phone based context-aware traffic state estimation framework. / Minh, Quang Tran; Baharudin, Muhammad Ariff; Kamioka, Eiji.

In: Journal of Information Processing, Vol. 21, No. 1, 2013, p. 76-89.

Research output: Contribution to journalArticle

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