Context-Aware Mobile Intelligent Transportation Systems

Abstract

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.