An Agentmining Framework for Intelligent Vehicular System

Abstract

Abstract During the last decades, the evolution of wireless technologies has allowed researches to design communication systems where vehicles participate in the communication networks. Vehicular Ad-Hoc Network is an important component of Intelligent Transportation System, which has a future potential in terms of a rich set of applications that it can provide to its commuters. Various approaches has been proposed in recent years for the design of intelligent VANET but most of the proposed works are limited to provide a complete road information to vehicles. For this reason, to develop a sophisticated framework which should disseminate up-to-the minute information about existing or impending traffic-related events has gained recent attention. The proposed framework exploit concepts data mining, machine learning and agent technology to model intelligent vehicular system contributes safer and more efficient roads by providing timely information and decision making capability to vehicle driver. Our work shows the techniques and methods resulting from the field of agent mining applied to many aspects including intelligent traffic control, dynamic routing, congestion management, decision support, modeling and simulation. The current research related to design a shell to control and monitor vehicles by sending intelligent traffic report and warning messages. Simulation results shows communication among agents in a collaborative environment.