Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

• Main Authors: Uglanov, A., Kartashev, K., Campean, I. Felician, Doikin, A., Abdullatif, A., Angiolini, E., Lin, C., Zhang, Q.
• Other Authors: aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering
• Language: en
• Published: Springer 2021
• Subjects: Driver behaviour modelling; Probability density function; Kernel density estimation; Probabilistic predictions
• Online Access: http://hdl.handle.net/10454/18692

Yes === 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. === The full-text of this paper will be released for public view at the end of the publisher embargo on 18 Nov 2023.