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dc.contributor.authorKartashev, K.
dc.contributor.authorDoikin, Aleksandr
dc.contributor.authorCampean, I. Felician
dc.contributor.authorUglanov, A.
dc.contributor.authorAbdullatif, Amr R.A.
dc.contributor.authorZhang, Q.
dc.contributor.authorAngiolini, E.
dc.date.accessioned2021-12-10T17:27:38Z
dc.date.accessioned2021-12-21T15:00:11Z
dc.date.available2021-12-10T17:27:38Z
dc.date.available2021-12-21T15:00:11Z
dc.date.issued2022
dc.identifier.citationKartashev 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.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18694
dc.descriptionNoen_US
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/978-3-030-87094-2_49en_US
dc.subjectMCL algorithmen_US
dc.subjectDiscrete pdfen_US
dc.subjectDivergenceen_US
dc.titleDriver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithmen_US
dc.status.refereedYesen_US
dc.date.Accepted2021-10-09
dc.contributor.sponsoraiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering.
dc.date.application2021-11-18
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US
dc.date.updated2021-12-10T17:27:40Z
refterms.dateFOA2021-12-21T15:00:33Z
dc.openaccess.statusclosedAccessen_US


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