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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This research was supported by the Swiss National Science Foundation (SNF) within NRP 71 “Managing energy consumption” and by the Commission for Technology and Innovation (CTI) within the Swiss Competence Center for Energy Research (SCCER) Mobility.
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Bucher, D., Mangili, F., Bonesana, C. et al. Demo Abstract: Extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. Comput Sci Res Dev 33, 267–268 (2018). https://doi.org/10.1007/s00450-017-0375-2
- Trajectory Analysis