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Demo Abstract: Extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behaviour

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Bucher Dominik, Mangili Francesca, Bonesana Claudio, Cellina Francesca, Jonietz David, Raubal Martin,
Project GoEco! A community based eco-feedback approach to promote sustainable personal mobility styles
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Original article (peer-reviewed)

Journal Computer Science-Research and Development
Volume (Issue) 33(1-2)
Page(s) 267 - 268
Title of proceedings Computer Science-Research and Development
DOI 10.1007/s00450-017-0375-2

Open Access

Type of Open Access Repository (Green Open Access)


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 energyefficient 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.