Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

Abstract

This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation.