Predicting future vehicle usage based on the analysis of CAN bus data is a popular data mining application. Many of the usage indicators, like the utilization hours, are non-stationary time series. To predict their values, recent approaches based on Machine Learning combine multiple data features describing engine status, travels, and roads. While most of the proposed solutions address cars and trucks usage prediction, a smaller body of work has been devoted to industrial and construction vehicles, which are usually characterized by more complex and heterogeneous usage patterns. This paper describes a real case study performed on a 4-year CAN bus dataset collecting usage data about 2 250 construction vehicles of various types and models. We apply a statistics-based approach to select the most discriminating data features. Separately for each vehicle, we train regression algorithms on historical data enriched with contextual information. The achieved results demonstrate the effectiveness of the proposed solution.

Heterogeneous industrial vehicle usage predictions: A real case / Markudova, D.; Baralis, E.; Cagliero, L.; Mellia, M.; Vassio, L.; Amparore, E.; Loti, R.; Salvatori, L.. - ELETTRONICO. - 2322:(2019), pp. 1-6. ((Intervento presentato al convegno 2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019 tenutosi a Lisbona (Portugal) nel 2019.

Heterogeneous industrial vehicle usage predictions: A real case

Markudova D.;Baralis E.;Cagliero L.;Mellia M.;Vassio L.;
2019

Abstract

Predicting future vehicle usage based on the analysis of CAN bus data is a popular data mining application. Many of the usage indicators, like the utilization hours, are non-stationary time series. To predict their values, recent approaches based on Machine Learning combine multiple data features describing engine status, travels, and roads. While most of the proposed solutions address cars and trucks usage prediction, a smaller body of work has been devoted to industrial and construction vehicles, which are usually characterized by more complex and heterogeneous usage patterns. This paper describes a real case study performed on a 4-year CAN bus dataset collecting usage data about 2 250 construction vehicles of various types and models. We apply a statistics-based approach to select the most discriminating data features. Separately for each vehicle, we train regression algorithms on historical data enriched with contextual information. The achieved results demonstrate the effectiveness of the proposed solution.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2751658
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