International Journal of Artificial Intelligence and Neural Networks
Author(s) : GEORGE DIMITRAKOPOULOS
The ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards the Future Internet (FI) era, which is characterized, among others, by powerful and complex network infrastructures, and innovative applications, services and content. An area of applications that finds prosperous ground in the FI era lies in the world of transportation. In particular, recent and future ICT findings are envisaged to contribute to the enhancement of transportation efficiency at various levels, such as traffic, parking, safety and emergency management. In this context, the goal of this paper is to introduce an Intelligent Transportation System (ITS) that utilizes (i) the driver’s preferences, (ii) information extracted from the vehicle sensors, and (iii) previous knowledge and experience, in proposing adaptations of the vehicle’s driving style, in an automated manner. Knowledge is obtained through the exploitation of Bayesian networking concepts and specifically the Naïve-based model. Some indicative simulation results showcase the effectiveness of the proposed system, the advantage of which lies in that the reliability of the knowledge-based selection decisions is higher.