Extracting eco-feedback information fromautomatic activity tracking to promote energy-efficient individualmobility behavior

Bucher, Dominik and Mangili, Francesca and Bonesana, Claudio and Cellina, Francesca and Jonietz, David and Raubal, Martin (2017) Extracting eco-feedback information fromautomatic activity tracking to promote energy-efficient individualmobility behavior. In: Computer Science Research and Development - Special Issue EnergieInformatik 2017, October, 5-6 2017, Lugano.

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Abstract

Nowadays, most people own a smartphone which is well suited to constantly record the movement of its user. One use of the gathered mobility data is to provide users with feedback and suggestions for personal behavior change. Such eco-feedback on mobility patterns may stimulate users to adopt more energy-efficient mobility choices. In this paper, we present a methodology to extract mobility patterns from users' trajectories, compute alternative transport options, and aggregate and present them in an intuitive way. The resulting eco-feedback helps people understand their mobility choices and explore sustainable alternatives.

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