Current heavy vehicles are equipped with hundreds of sensors that are used to continuously collect data in motion. The logged data enables researchers and industries to address three main transportation issues related to performance (e.g. fuel consumption, breakdown), environment (e.g., emission reduction), and safety (e.g. reducing vehicle accidents and incidents during maintenance activities). While according to the American Transportation Research Institute (ATRI), the operational cost of heavy vehicles is around 59 % \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$59\%$$\end{document} of overall costs, there are limited studies demonstrating the specific impacts of external factors (e.g. weather and road conditions, driver behavior) on vehicle performance. In this work, vehicle usage modeling was studied based on time to determine the different usage styles of vehicles and how they can affect vehicle performance. An ensemble clustering approach was developed to extract vehicle usage patterns and vehicle performance taking into consideration logged vehicle data (LVD) over time. Analysis results showed a strong correlation between driver behavior and vehicle performance that would require further investigation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Usage Extraction Using Unsupervised Ensemble Approach


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    Proceedings of SAI Intelligent Systems Conference ; 2022 ; Amsterdam, The Netherlands September 01, 2022 - September 02, 2022



    Publication date :

    2022-08-31


    Size :

    17 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Engine Usage Analysis Using Unsupervised Machine Learning

    Cerqueira, S. / Morel, H. | British Library Conference Proceedings | 2023



    Unsupervised Weight Parameter Estimation Method for Ensemble Learning

    Uchida, M. / Maehara, Y. / Shioya, H. | British Library Online Contents | 2011