Empowering Commercial Vehicles through Data-Driven Methodologies

Abstract

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.

Classification of Factors Used to Predict Vehicle Break down in Commercial Vehicles

“…Additionally, the paper highlights how these technologies contribute to safer and more efficient commercial vehicle operations. Bethaz, et. al., (2021) [34] focus on the use of data-driven approaches to enhance the performance and capabilities of commercial vehicles.…”

“…Bethaz, et. al., (2021) [34] focus on the use of data-driven approaches to enhance the performance and capabilities of commercial vehicles. It explores how data analytics, real-time monitoring, and predictive methodologies can empower fleet operators to make informed decisions.…”