Uncertain low penetration rate - A practical issue in mobile intelligent transportation systems

Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Low penetration rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low penetration rate. This mechanism also reduces the critical penetration rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the penetration rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

Original languageEnglish
Title of host publicationProceedings - 26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012
Pages237-244
Number of pages8
DOIs
Publication statusPublished - 2012 May 14
Event26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012 - Fukuoka, Japan
Duration: 2012 Mar 262012 Mar 29

Publication series

NameProceedings - International Conference on Advanced Information Networking and Applications, AINA
ISSN (Print)1550-445X

Conference

Conference26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012
CountryJapan
CityFukuoka
Period12/3/2612/3/29

Keywords

  • ANN
  • GA
  • ITS
  • M-ITS
  • genetic algorithm
  • low penetration rate
  • mobile probes
  • neural network

ASJC Scopus subject areas

  • Engineering(all)

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