Abstract
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 data-driven 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.
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Acknowledgment
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. They also acknowledge the support of their home institution Plekhanov Russian University of Economics, Moscow. This research was supported by aiR-FORCE project, IDE, UK.
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Uglanov, A. et al. (2022). Driver Behaviour Modelling: Travel Prediction Using Probability Density Function. 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_48
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DOI: https://doi.org/10.1007/978-3-030-87094-2_48
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