Dynamic prediction of drivers' personal routes through machine learning

Abstract

Personal route prediction (PRP) has attracted much research interest recently because of its technical challenges and broad applications in intelligent vehicle and transportation systems. Traditional navigation systems generate a route for a given origin and destination based on either shortest or fastest route schemes. In practice, different people may very likely take different routes from the same origin to the same destination. Personal route prediction attempts to predict a driver's route based on the knowledge of driver's preferences. In this paper we present an intelligent personal route prediction system, I_PRP, which is built based upon a knowledge base of personal route preference learned from driver's historical trips. The I_PRP contains an intelligent route prediction algorithm based on the first order Markov chain model to predict a driver's intended route for a given pair of origin and destination, and a dynamic route prediction algorithm that has the capability of predicting driver's new route after the driver departs from the predicted route.