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    Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

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    Publication date
    2022
    Author
    Kartashev, K.
    Doikin, Aleksandr
    Campean, I. Felician
    Uglanov, A.
    Abdullatif, Amr R.A.
    Zhang, Q.
    Angiolini, E.
    Keyword
    MCL algorithm
    Discrete pdf
    Divergence
    Peer-Reviewed
    Yes
    Open Access status
    closedAccess
    
    Metadata
    Show full item record
    Abstract
    This paper presents a novel approach for probabilistic clustering, motivated by a real-world problem of modelling driving behaviour. The main aim is to establish clusters of drivers with similar journey behaviour, based on a large sample of historic journeys data. The proposed approach is to establish similarity between driving behaviours by using the Kullback-Leibler and Jensen-Shannon divergence metrics based on empirical multi-dimensional probability density functions. A graph-clustering algorithm is proposed based on modifications of the Markov Cluster algorithm. The paper provides a complete mathematical formulation, details of the algorithms and their implementation in Python, and case study validation based on real-world data.
    URI
    http://hdl.handle.net/10454/18694
    Version
    No full-text in the repository
    Citation
    Kartashev K, Doikin A, Campean IF et al (2022) Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm. In: Jansen T, Jensen R, Mac Parthalain N et al (Eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. Springer, Cham. 1409: 557-568.
    Link to publisher’s version
    https://doi.org/10.1007/978-3-030-87094-2_49
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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