Context-aware mobile intelligent transportation systems

Minh Quang Tran, Muhammad Ariff Baharudin, Eiji Kamioka

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


This paper proposes a practical quantification model for mobile phone based traffic state estimation systems (M-TES). The low penetration rate issue, an inherent issue impeding the realization of a mobile phone based application such as the M-TES, is thoroughly discussed. A notable solution framework, namely the intelligent context-aware velocity-density inference circuit (ICIC), is proposed to effectively resolve the low penetration rate issue. In the ICIC model, velocities and densities calculated directly from the sensed data and inferred by using different inference models such as the Greeshields or the moving average model are appropriately integrated. In addition, appropriate contexts extracted from data reported by mobile devices are utilized to identify the optimal estimation parameters leading to the optimal estimation effectiveness. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

Original languageEnglish
Title of host publicationIEEE Vehicular Technology Conference
Publication statusPublished - 2012
Event76th IEEE Vehicular Technology Conference, VTC Fall 2012 - Quebec City, QC
Duration: 2012 Sep 32012 Sep 6


Other76th IEEE Vehicular Technology Conference, VTC Fall 2012
CityQuebec City, QC



  • ANN
  • Context-aware
  • GA
  • Genetic algorithm
  • ITS
  • Low penetration rate
  • M-ITS
  • Mobile probes
  • Neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Applied Mathematics

Cite this

Tran, M. Q., Baharudin, M. A., & Kamioka, E. (2012). Context-aware mobile intelligent transportation systems. In IEEE Vehicular Technology Conference [6398916]