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Vehicle Usage Extraction Using Unsupervised Ensemble Approach
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-3797-4605
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
Arriver Software AB, a Qualcomm Company, Linköping, Sweden.
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2022 (Engelska)Ingår i: Proceedings of SAI Intelligent Systems Conference, Springer, 2022, s. 588-604Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

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%59% 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.

Ort, förlag, år, upplaga, sidor
Springer, 2022. s. 588-604
Serie
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 542
Nationell ämneskategori
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Identifikatorer
URN: urn:nbn:se:mau:diva-54745DOI: 10.1007/978-3-031-16072-1_43ISI: 000890312800043Scopus ID: 2-s2.0-85137975588ISBN: 978-3-031-16071-4 (tryckt)ISBN: 978-3-031-16072-1 (digital)OAI: oai:DiVA.org:mau-54745DiVA, id: diva2:1693275
Konferens
Intelligent Systems and Applications, 1-2 September 2022, Amsterdam, The Netherlands
Tillgänglig från: 2022-09-06 Skapad: 2022-09-06 Senast uppdaterad: 2024-02-05Bibliografiskt granskad

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Khoshkangini, Reza

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Khoshkangini, RezaTajgardan, Mohsen
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Institutionen för datavetenskap och medieteknik (DVMT)Internet of Things and People (IOTAP)
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