PosterPDF Available

Extracting Eco-Feedback from Movement Trajectories

Authors:
  • c.technology AG

Abstract

GoEco! is one of several smartphone applications that perform automatic mobility tracking. In contrast to many others, it uses the tracked movement data to compute possible behavioral improvements of its users, and provides this assessment as eco-feedback in various forms. These include booklets detailing user journeys and possible alternatives in detail, an in-app feedback screen which summarizes the information given in the booklets, as well as gamification elements that use the computed improvements as a base to compute progress towards goals and challenges, award trophies and allow people to compete against each other. This poster discusses the various steps involved in producing comprehensive yet easy to communicate ecofeedback from the raw movement data, and introduces estimations of potential CO2 savings and preliminary findings from providing the users of GoEco! with this eco-feedback.
Swiss Competence Center for Energy Research
Efficient Technologies and Systems for Mobility
Partners
References
To compute them, we aggregate routes into loops
starting and ending at the user’s home location. Out of
all the loops identified for a given user, we consider
those that appear at least 3 times in 6 weeks as
systematic.
Introduction Mobility Features and Patterns
GoEco! is one of several smartphone applications that perform
automatic mobility tracking. In contrast to many others, it uses the
tracked movement data to compute possible behavioral improvements
of its users, and provides this assessment as eco-feedback in various
forms. These include booklets detailing user journeys and possible
alternatives in detail, an in-app feedback screen which summarizes the
information given in the booklets, as well as gamification elements that
use the computed improvements as a base to compute progress
towards goals and challenges, award trophies and allow people to
compete against each other. This poster discusses the various steps
involved in producing comprehensive yet easy to communicate eco-
feedback from the raw movement data, and introduces estimations of
potential CO2savings and preliminary findings from providing the users
of GoEco! with this eco-feedback.
Dominik Bucher, David Jonietz, Martin Raubal
Institute of Cartography and Geoinformation, ETH Zurich
Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland
dobucher@ethz.ch, jonietzd@ethz.ch, mraubal@ethz.ch
Francesca Mangili, Claudio Bonesana
Dalle Molle Institute for Artificial Intelligence, USI - SUPSI
Galleria 2, 6928 Manno, Switzerland
francesca@idsia.ch, claudio@idsia.ch
Francesca Cellina
Insitute for Applied Sustainability to the Built Environment, SUPSI
via Trevano, 6952 Canobbio, Switzerland
francesca.cellina@supsi.ch
Ecological Alternatives
For each systematic loop, we assess other travel options
that visit the same sequence of points of interest and
reduce the overall ecological impact (at least 5% less
CO2produced, to account for tracking inaccuracies), yet
still respect the peculiarities of daily mobility:
users only have a limited number of transport
modes available, and these have to end up at the
same place where they were originally taken from;
the overall travel duration should not increase
excessively.
The computed travel options form a graph of mobility
choices from which we choose the one producing the
least CO2as a possible alternative for all the loops that
correspond to this systematic loop.
Figure 1 shows an exemplary alternative for a walk/tram
route, which consists of taking the bike instead. This
leads to a reduction in CO2production of 0.1 kg every
time the user chooses the alternative.
Communication of Eco-Feedback
As GoEco! is installed on every participant’s phone, the
app itself is the primary means to provide eco-feedback.
Figure 2 shows the weekly summary for a sample user.
Due to the complexity of communicating patterns,
loops, and possible alternatives, the in-app feedback
screen focuses on mobility features and their changes
over the weeks. Nonetheless, the systematic mobility is
used as a base for the gamified elements, in particular
goal-setting, whose most ambitious suggestions
correspond to using the most ecological alternative for
every loop.
For users with a high interest in their own mobility, the
eco-feedback booklet provides maps (as in Figure 1) and
detailed records of the chosen routes and detected
loops, their best alternatives, and the potential savings
from a mobility behavior change. These booklets are
sent to users by e-mail.
Expected Impact
We identified an average of 3.98 systematic loops per
user, each one repeated 4.86 times during a 6-week
period. Looking at the potential CO2savings of the
whole GoEco! participants sample (computed using the
method described on this poster), we see a possible
reduction from approx. 35 kg CO2/ week per user to
around 20 kg CO2/ week per user (median).
While it might be unrealistic to reach these numbers (as
mobility is still highly individual and depending on many
contextual factors that were not considered in the
computation of eco-feedback), preliminary analyses
show that people with a high potential for change
(covering most distances by car, e.g., in the Southern
part of Switzerland) significantly change their behavior
after receiving the eco-feedback presented on this
poster.
[1] Froehlich, J. et al.: Ubigreen: investigating a mobile tool for
tracking and supporting green transportation habits. In:
Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, pp. 1043-1052. ACM (2009)
[2] Froehlich, J., Findlater, L., Landay, J.: The design of eco-feedback
technology. In: Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, pp. 1999-2008. ACM
(2010)
[3] Bucher, D. et al.: Demo Abstract: Extracting Eco-Feedback
Information from Automatic Activity Tracking to Promote
Energy-Efficient Individual Mobility Behavior. In: Computer
Science - Research and Development (6th D-A-CH+ Energy
Informatics Conference). (2017)
Extracting Eco-Feedback from Movement Trajectories
In automatic mobility tracking apps [1], users frequently
see a very condensed summary of their mobility (e.g.,
total CO2emissions) or all the individual routes they
travel, and do not get a complete yet simple
understanding of their mobility patterns. Getting the
right eco-feedback [2], and making users aware of their
mobility patterns and the consequences they entail, is
acknowledged as a necessary though not sufficient —
condition towards more sustainable mobility [1].
Eco-feedback can be improved by taking into account
peculiarities of individual mobility. In our approach, we
first identify users’ individual mobility features and
patterns and then compute ecological travel alternatives.
We deployed this approach in the Swiss-based GoEco!
project, which uses a gamified smartphone app to
influence the mobility behavior of 213 volunteer users
over the course of 4.5 months. This poster is based on
the work presented in [3].
Mobility features are aggregated indicators summarizing
user’s mobility data as a whole, such as the weekly
distribution of transport modes, the average distance
traveled, or the CO2produced.
Mobility patterns instead describe systematically
traveled routes. They are of particular interest, as a
behavioral change in these situations would be repeated
over time and thus has a large potential to reduce
energy consumption.
Figure 1 A systematic loop (A)
and the ecological alternative we computed (B).
Figure 2 In-app eco-feedback visualization.
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