Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

Description

No / 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.

Links & Downloads

1. 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.
2. http://hdl.handle.net/10454/18694
3. https://doi.org/10.1007/978-3-030-87094-2_49

Tags

MCL algorithm

Discrete pdf

Divergence

Additional Fields

Identifer	oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18694
Date	10 December 2021
Creators	Kartashev, K., Doikin, Aleksandr, Campean, Felician, Uglanov, A., Abdullatif, Amr R.A., Zhang, Q., Angiolini, E.
Contributors	aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering.
Source Sets	Bradford Scholars
Language	English
Detected Language	English
Type	Conference paper, No full-text in the repository