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Driver Behaviour Modelling: Travel Prediction Using Probability Density Function
Kartashev, K. Doikin, Aleksandr Abdullatif, Amr R.A. Angiolini, E. Lin, C. Zhang, Q.
Kartashev, K.
Doikin, Aleksandr
Abdullatif, Amr R.A.
Angiolini, E.
Lin, C.
Zhang, Q.
Publication Date
2022
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Peer-Reviewed
Yes
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closedAccess
Accepted for publication
2021-09-10
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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.
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Citation
Uglanov A, Kartashev K, Campean IF et al (2022) Driver Behaviour Modelling: Travel Prediction Using Probability Density Function. In: Jansen T, Jensen R, Mac Parthaláin N et al (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. Springer, Cham. 1409: 545-556.
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Conference paper