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Driving Sustainably – The Influence of IoT-based Eco-Feedback on Driving Behavior

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Title:Driving Sustainably – The Influence of IoT-based Eco-Feedback on Driving Behavior
Authors:Bätz, Alexander
Gimpel, Henner
Heger, Sebastian
Wöhl, Moritz
Keywords:Analytics and Decision Support for Green IS and Sustainability Applications
driving behavior
eco-feedback
factor model
iot
show 1 morereal-world data
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Date Issued:07 Jan 2020
Abstract:One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by eco-feedback. Yet, understanding driving behavior is important to target personalized recommendations towards re-duced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that eco-feedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63853
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.114
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Analytics and Decision Support for Green IS and Sustainability Applications


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