An Adaptable Four-Dimensional Destination Predictor for Smart Vehicles

Nardine Basta, Amal El-Nahas, Hans Peter Großmann, Amr El Mougy, Slim Abdennadher

Abstract: In the recent years, intelligent vehicles became the most common everyday task that attracts a great interest from the industry. This work is dedicated to enhance the vehicles' intelligence through an adaptable four dimensional model capable of impersonating the vehicle driver by accurately predicting the time and location of destinations. Existing research efforts have addressed this challenge through prediction models that consider one or two aspects of human behavior, such as the social network or location semantics. However, the accuracy of these models remains rather limited. Accordingly, this paper addresses the shortfalls of the existing work by incorporating most aspects of human mobility. Moreover, we study the impact of each of the model dimensions on the prediction accuracy, individually and combined. We further propose an optimization algorithm to calculate the best blend of the model dimensions, that maximizes the prediction accuracy. The performance of the model is evaluated using a dataset that is collected from users in the city of UIm, Germany, as well as through computer simulations. The results show that the model can achieve a prediction accuracy of 95%, outperforming the state-of-the-art counterparts.