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Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

Kartashev, K.
Doikin, Aleksandr
Abdullatif, Amr R.A.
Zhang, Q.
Angiolini, E.
Publication Date
2022
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2021-10-09
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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.
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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.
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