MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT
In the dynamic landscape of modern business operations, ensuring economic security through efficient and intelligent fleet management is imperative. This necessitates a dual focus on safeguarding revenue streams and optimizing operational costs. The aim of this study centers on two main objectives:...
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| Format: | Article |
| Language: | English |
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Academy of Economic Studies of Moldova (AESM), Center for Studies in European Integration
2024-06-01
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| Series: | Eastern European Journal of Regional Studies |
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| Online Access: | https://csei.ase.md/journal/files/issue_101/EEJRS_Issue10.1_page-79-97.pdf |
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| _version_ | 1849397631279693824 |
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| author | Coralia TANASUICA (ZOTIC) Mihai Daniel ROMAN |
| author_facet | Coralia TANASUICA (ZOTIC) Mihai Daniel ROMAN |
| author_sort | Coralia TANASUICA (ZOTIC) |
| collection | DOAJ |
| description | In the dynamic landscape of modern business operations, ensuring economic security through efficient and intelligent fleet management is imperative. This necessitates a dual focus on safeguarding revenue streams and optimizing operational costs. The aim of this study centers on two main objectives: first, to identify driving behaviors that have a substantial impact on vehicle maintenance costs; second, to ensure the sustainability of the fleet is managed effectively. To achieve these objectives, the research employs unsupervised Machine Learning (ML) techniques for segmenting driving styles based on diverse parameters collected from Internet of Things (IoT) devices. Furthermore, the Long Short-Term Memory (LSTM) algorithm is used for forecasting fuel consumption, offering a predictive glance into future expenditures. The methodology is based on the analysis of data gathered from sensors installed on the vehicle's Controller Area Network (CAN), collected over a span of five months. The findings spotlight a subset of drivers whose aggressive driving significantly influences maintenance costs and highlight optimal indicators for drivers to monitor to minimize CO2 emissions. Additionally, the study identifies key performance indicators that drivers should monitor to reduce CO2 emissions, contributing to the environmental sustainability of the fleet. This investigation not only elucidates the financial and environmental implications of driving behaviors but also showcases the transformative potential of ML technologies in enhancing the strategic management of vehicle fleets. Through this exploration, the research advocates for the integration of advanced analytics and sustainable practices as foundational elements for businesses striving to achieve economic security and operational resilience. |
| format | Article |
| id | doaj-art-ba4969fccef44b4bbf22a933cf6ad0d9 |
| institution | Kabale University |
| issn | 1857-436X 2537-6179 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Academy of Economic Studies of Moldova (AESM), Center for Studies in European Integration |
| record_format | Article |
| series | Eastern European Journal of Regional Studies |
| spelling | doaj-art-ba4969fccef44b4bbf22a933cf6ad0d92025-08-20T03:38:55ZengAcademy of Economic Studies of Moldova (AESM), Center for Studies in European IntegrationEastern European Journal of Regional Studies1857-436X2537-61792024-06-011017997https://doi.org/10.53486/2537-6179.10-1.05MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENTCoralia TANASUICA (ZOTIC)0https://orcid.org/0009-0003-6570-4375Mihai Daniel ROMAN1https://orcid.org/0000-0002-3859-7629Bucharest University of Economic StudiesBucharest University of Economics StudiesIn the dynamic landscape of modern business operations, ensuring economic security through efficient and intelligent fleet management is imperative. This necessitates a dual focus on safeguarding revenue streams and optimizing operational costs. The aim of this study centers on two main objectives: first, to identify driving behaviors that have a substantial impact on vehicle maintenance costs; second, to ensure the sustainability of the fleet is managed effectively. To achieve these objectives, the research employs unsupervised Machine Learning (ML) techniques for segmenting driving styles based on diverse parameters collected from Internet of Things (IoT) devices. Furthermore, the Long Short-Term Memory (LSTM) algorithm is used for forecasting fuel consumption, offering a predictive glance into future expenditures. The methodology is based on the analysis of data gathered from sensors installed on the vehicle's Controller Area Network (CAN), collected over a span of five months. The findings spotlight a subset of drivers whose aggressive driving significantly influences maintenance costs and highlight optimal indicators for drivers to monitor to minimize CO2 emissions. Additionally, the study identifies key performance indicators that drivers should monitor to reduce CO2 emissions, contributing to the environmental sustainability of the fleet. This investigation not only elucidates the financial and environmental implications of driving behaviors but also showcases the transformative potential of ML technologies in enhancing the strategic management of vehicle fleets. Through this exploration, the research advocates for the integration of advanced analytics and sustainable practices as foundational elements for businesses striving to achieve economic security and operational resilience.https://csei.ase.md/journal/files/issue_101/EEJRS_Issue10.1_page-79-97.pdfdriver behaviorfleet sustainabilityunsupervised machine learningclustering analysisco2 forecasting |
| spellingShingle | Coralia TANASUICA (ZOTIC) Mihai Daniel ROMAN MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT Eastern European Journal of Regional Studies driver behavior fleet sustainability unsupervised machine learning clustering analysis co2 forecasting |
| title | MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT |
| title_full | MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT |
| title_fullStr | MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT |
| title_full_unstemmed | MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT |
| title_short | MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT |
| title_sort | machine learning for concrete sustainability improvement smart fleet management |
| topic | driver behavior fleet sustainability unsupervised machine learning clustering analysis co2 forecasting |
| url | https://csei.ase.md/journal/files/issue_101/EEJRS_Issue10.1_page-79-97.pdf |
| work_keys_str_mv | AT coraliatanasuicazotic machinelearningforconcretesustainabilityimprovementsmartfleetmanagement AT mihaidanielroman machinelearningforconcretesustainabilityimprovementsmartfleetmanagement |