Empowering Commercial Vehicles through Data-Driven Methodologies

In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows...

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Bibliographic Details
Published in:Electronics (Basel) 2021-10, Vol.10 (19), p.2381
Main Authors: Bethaz, Paolo, Cavaglion, Sara, Cricelli, Sofia, Liore, Elena, Manfredi, Emanuele, Salio, Stefano, Regalia, Andrea, Conicella, Fabrizio, Greco, Salvatore, Cerquitelli, Tania
Format: Article
Language:English
Subjects:
Algorithms
Breakdowns
Commercial vehicles
Cost analysis
Cost control
Customer satisfaction
Data science
Data transmission
Decision analysis
Decision making
Heavy duty trucks
Internet of Things
Machine learning
Maintenance
Sensors
Telematics
Vehicles
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Summary:In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TETRAPAC, offers a generalizable approach to estimate vehicle health conditions based on monitored features enriched by innovative key performance indicators. We discussed performance of TETRAPAC in two real-life settings related to trucks. The obtained results in both tasks are promising and able to support the company’s decision-making process in the planning of maintenance interventions.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10192381