In the age of connected vehicles, large amounts of data can be collected while driving through a variety of on-board sensors. The information collected can be used for various types of data-driven analytics that can be of great benefit to both vehicle owners, e.g., to reduce costs by means of predictive maintenance, and to society as a whole, e.g., to optimize mobility behavior. Prior to any real-world data analysis, an investigation and characterization of the available data is of utmost importance in order to evaluate the quality and quantity of the data and to set the right expectations. In this paper, we focus on the data exploration and characterization step, which is necessary to avoid inconsistencies in the collected parameters and to enable valid, data-driven modeling. The proposed data exploration considers both the frequency of samples and their values for all monitored parameters. A specific cross-provider data comparison is performed to compare values collected for the same vehicle at the same time from different fleet monitoring data providers. The study is applied to a real-world use case with months of data from dozens of vehicles deployed in the waste collection service managed by SEA, Soluzioni Eco Ambientali, in Italy. The analyzes uncover unexpected behaviors in the measurements and lead to their early identification, bringing great benefits to the company operating the fleet by improving data collection and enabling a safe modeling phase.

Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers / Monaco, Simone; Bethaz, Paolo; Apiletti, Daniele; Baldini, Fabrizio Pio; Caso, Carlo; Cerquitelli, Tania. - (2022). ((Intervento presentato al convegno EDBT/ICDT Workshop, 6th International workshop on Data Analytics solutions for Real-LIfe APplications tenutosi a Edinburgh nel March 29 - April 1, 2022.

Exploring waste-collection fleet data: challenges in a real-world use case from multiple data providers

Monaco, Simone;Bethaz, Paolo;Apiletti, Daniele;Cerquitelli, Tania
2022

Abstract

In the age of connected vehicles, large amounts of data can be collected while driving through a variety of on-board sensors. The information collected can be used for various types of data-driven analytics that can be of great benefit to both vehicle owners, e.g., to reduce costs by means of predictive maintenance, and to society as a whole, e.g., to optimize mobility behavior. Prior to any real-world data analysis, an investigation and characterization of the available data is of utmost importance in order to evaluate the quality and quantity of the data and to set the right expectations. In this paper, we focus on the data exploration and characterization step, which is necessary to avoid inconsistencies in the collected parameters and to enable valid, data-driven modeling. The proposed data exploration considers both the frequency of samples and their values for all monitored parameters. A specific cross-provider data comparison is performed to compare values collected for the same vehicle at the same time from different fleet monitoring data providers. The study is applied to a real-world use case with months of data from dozens of vehicles deployed in the waste collection service managed by SEA, Soluzioni Eco Ambientali, in Italy. The analyzes uncover unexpected behaviors in the measurements and lead to their early identification, bringing great benefits to the company operating the fleet by improving data collection and enabling a safe modeling phase.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2961276