A Sensor-Based Application for Eco-Driving Management in Short-Term Car Rentals

Michał Adamczak (), Adrianna Toboła-Walaszczyk, Piotr Cyplik, Łukasz Nowak and Maciej Tórz
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Michał Adamczak: Chair of Operations Management, Poznan School of Logistics, 61-755 Poznan, Poland
Adrianna Toboła-Walaszczyk: Chair of Operations Management, Poznan School of Logistics, 61-755 Poznan, Poland
Piotr Cyplik: Faculty of Engineering Management, Poznan University of Technology, 60-965 Poznan, Poland
Łukasz Nowak: Independent Researcher, 96-321 Żelechów, Poland
Maciej Tórz: Rentis S.A., 62-081 Wysogotowo, Poland

Sustainability, 2024, vol. 16, issue 9, 1-17

Abstract: How to reduce fuel consumption to mitigate CO 2 emissions to the atmosphere and improve road safety is one of the priorities to be addressed in the field of transport in the European Union. Considering the trend towards more frequent car rentals, it seems important to encourage drivers to change their driving style to a more ecological and economic one. This can be achieved by a system (built of a sensor located in the car, analytical software in the cloud and a mobile application for displaying results) that analyzes driving style and tells the driver how to drive better. Solutions such as the car bus PCB, GSM/GPS modem and 3D sensors were used in the development of the sensor. The validation of the sensor and the development of the analytical system are based on tests carried out in road conditions and in a closed area. Graphical methods (box-plot charts), correlation analysis and testing statistical hypotheses using the Mann–Whitney method were used in the analysis of the test results. The developed sensor and the analytical system allow for identifying the driving style of drivers. This system, through the use of a sensor that allows for downloading data not only from the car’s CAN bus but also the forces acting on the vehicle, permits the checking of 14 driving parameters used to interpret the driver’s driving style.

Keywords: CO 2 emission reduction; driving style identification; IoT system validation; driving style analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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