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

 
: Bätz, Alexander; Gimpel, Henner; Heger, Sebastian; Wöhl, Moritz

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Volltext ()

Hawaii International Conference on System Sciences 2020 : 06-10 January 2020, Honolulu, Hawaii; Proceedings of the 53rd Annual Hawaii International Conference on System Sciences
Honolulu: ScholarSpace, 2020
ISBN: 978-0-9981331-3-3
S.912-921
Hawaii International Conference on System Sciences (HICSS) <53, 2020, Honolulu/Hawaii>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()
eco-feedback; driving behavior; real-world data; factor analysis

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 to-wards reduced 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. 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 reduced 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.

: http://publica.fraunhofer.de/dokumente/N-599861.html