• Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

      Kartashev, K.; Doikin, Aleksandr; Campean, I. Felician; Uglanov, A.; Abdullatif, Amr R.A.; Zhang, Q.; Angiolini, E.; aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering. (2022)
      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.
    • Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

      Uglanov, A.; Kartashev, K.; Campean, I. Felician; Doikin, Aleksandr; Abdullatif, Amr R.A.; Angiolini, E.; Lin, C.; Zhang, Q.; aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering (2022)
      This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation.