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    Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

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    Publication date
    2022
    Author
    Uglanov, A.
    Kartashev, K.
    Campean, I. Felician
    Doikin, Aleksandr
    Abdullatif, Amr R.A.
    Angiolini, E.
    Lin, C.
    Zhang, Q.
    Keyword
    Driver behaviour modelling
    Probability density function
    Kernel density estimation
    Probabilistic predictions
    Peer-Reviewed
    Yes
    Open Access status
    closedAccess
    
    Metadata
    Show full item record
    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.
    URI
    http://hdl.handle.net/10454/18692
    Version
    No full-text in the repository
    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.
    Link to publisher’s version
    https://doi.org/10.1007/978-3-030-87094-2_48
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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