Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

• Main Authors: Kartashev, K., Doikin, A., Campean, I. Felician, Uglanov, A., Abdullatif, A., Zhang, Q., Angiolini, E.
• Other Authors: aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering.
• Language: en
• Published: Springer 2021
• Subjects: MCL algorithm; Discrete pdf; Divergence
• Online Access: http://hdl.handle.net/10454/18694

Yes === 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. === The full-text of this paper will be released for public view at the end of the publisher embargo on 18 Nov 2023.