Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

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2022Author
Uglanov, Alexey
Kartashev, K.
Campean, Felician

Doikin, Aleksandr
Abdullatif, Amr R.A.
Angiolini, E.
Lin, C.
Zhang, Q.
Keyword
Driver behaviour modellingProbability density function
Kernel density estimation
Probabilistic predictions
Peer-Reviewed
YesOpen Access status
closedAccessAccepted for publication
2021-09-10
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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.Version
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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.Link to Version of Record
https://doi.org/10.1007/978-3-030-87094-2_48Type
Conference paperae974a485f413a2113503eed53cd6c53
https://doi.org/10.1007/978-3-030-87094-2_48