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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Acknowledgement
Alexey Uglanov and Kirill Kartashev acknowledge the support from the Erasmus+ Programme for their research placement with the Advanced Automotive Analytics research laboratory at the University of Bradford, and the support of their home institution Plekhanov Russian University of Economics, Moscow. This research was supported by aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering.
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Kartashev, K. et al. (2022). Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_49
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DOI: https://doi.org/10.1007/978-3-030-87094-2_49
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