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GoEco! - A Set of Smartphone Apps Supporting
the Transition Towards Sustainable Mobility
Patterns
Francesca Cellina
1
, Dominik Bucher
2
, Martin Raubal
2
, Roman Rudel
1
, Vanessa
de Luca3, and Massimo Botta3
1Insitute for Applied Sustainability to the Built Environment
SUPSI
Via Trevano, 6952 Canobbio, Switzerland
francesca.cellina@supsi.ch,roman.rudel@supsi.ch
2Institute of Cartography and Geoinformation
ETH Zurich
Stefano-Franscini-Platz 5,8093 Zurich, Switzerland
dobucher@ethz.ch,mraubal@ethz.ch
3Constructions and Design
SUPSI
Via Trevano, 6952 Canobbio, Switzerland
vanessa.deluca@supsi.ch,massimo.botta@supsi.ch
Abstract.
How can we encourage people to engage in more sustainable
mobility lifestyles, reducing car use? Taking advantage of the wide avail-
ability of smartphones, we overcome the traditional awareness-raising
approach and exploit eco-feedback, social norms and peer pressure ele-
ments in an ICT-based motivation system. We developed two smartphone
Apps, which are currently being tested in a real-life, large-scale living lab
experiment. The GoEco! Tracker App monitors the mobility patterns
of the participants, identifying the routes they travel and the means of
transport they use, and it is primarily meant to collect baseline data. Ex-
ploiting individual achievement and competition game mechanics, the full
GoEco! App additionally nudges users towards personal goals for change
and engages them in individual and collective challenges, strengthening
competition with a “hall of fame” section. In this paper we introduce
the GoEco! Apps and their theoretical eco-feedback and gamification
framework, describe their key functionalities and comment on the main
strengths and limitations after one month of large-scale testing of the
GoEco! Tracker App.
Keywords: Mobility tracking; App; Gamification; Sustainability;
1Introduction
The present urban transportation system, mostly tailored for cars, has long
shown its limitations [
7
]. In many urban areas, alternative and effective transport
2Francesca Cellina et al.
modes are already available and they could be used in intermodal combinations
to satisfy many travel needs [
6
]: public transportation, slow mobility networks,
vehicle-sharing systems. However, these transport modes still tend to be neglected
due to a deep-rooted car dependency [4].
How can we encourage people to engage in more sustainable mobility lifestyles,
reducing use of the car? We propose to overcome the traditional awareness-raising
approach and, building on recent research in social psychology and behavior
studies, to take advantage of the wide availability of smartphone devices (cf. [
11
]).
Can ICT-based eco-feedback, social norms and peer pressure be effective in
fostering changes in personal mobility behavior? To answer this question, we
designed GoEco!, a set of two smartphone applications, and are now testing them
in a “living lab” field study [2] involving around 600 volunteer real-life users in
Southern Switzerland and in the City of Zurich.
2The GoEco! Apps
We built two Apps: the first one, named GoEco! Tracker, monitors the mobility
patterns of its users, identifying the routes they travel and the means of transport
they use. Post-processing the data gathered by the tracker app allows us to
identify the present mobility patterns of the users (baseline data) and their
potentials for change. For this purpose, we determine their regular trips and
assess the feasibility of replacing their usual transport mode with a more energy-
efficient means. At this stage, feedback on people’s mobility choices is as limited
as possible. In order to collect non-biased baseline data, in fact, it would be even
better not to provide users with any feedback on their mobility choices. However,
considering present limitations in automatic mobility tracking [
3
], this cannot be
avoided completely because a validation for both the path they travel and the
means of transport they use is required.
The second App, named GoEco!, performs the same mobility tracking; in
addition, adopting a gamification approach based on individual achievement and
competitive game mechanics [
10
], it also nudges the users to define personal goals
and targets for change with respect to their baseline mobility patterns. On a daily
basis, the App provides feedback to the users regarding their mobility choices
(distance travelled, means of transport used, travelling time) and related impacts
(energy consumption and CO
2
emissions) (see Figure 1). On a weekly basis, the
App also indicates the progress towards their goals for change, comparing it with
achievements by other participants, invites them to take part in mobility chal-
lenges, suggests meaningful low-impact, alternative modal options and rewards
good performances and achievements with virtual prizes (badges). It is important
to note that change is always measured individually, i.e., in comparison to the
goals and baseline data of an individual participant. This implies that every
person defines her own rules.
Both Apps exploit the APIs of the commercial, free fitness tracker App
Moves
®
(https://dev.moves-app.com). Moves records a user’s position at various
points and is able to determine whether the user was walking, running, cycling,
GoEco! - Apps Supporting Sustainable Mobility 3
Fig. 1.
The start screen of the full GoEco! app. It shows an overview of the current
mobility behavior, as well as the achievements reached so far, and quick links to other
important parts of the app, such as Challenges,Badges,Reports, and Reference Data.
or taking another form of transport between track points. Because we need a
more fine-grained distinction between different modes of transport, we built a
post-processing algorithm, which utilizes a naive Bayes classifier [
9
] to distinguish
between modes such as bus, train, tram or car. Our classifier takes into account
several route characteristics, such as travel speed, acceleration or overlay between
visited points and the network of the public Swiss transportation system (stops
and lines) [
3
]. Every day users are asked to check and validate the means
of transport for every route tracked. The Bayes classifier uses validations to
constantly improve its future predictions, reducing the interactions with users as
time goes by.
3Mobility Patterns and Potentials for Change
The GoEco! Tracker App needs to be used for at least four weeks to collect
baseline data. After such period, participants get a report showing the routes they
traveled (Figure 2), summarizing their current mobility patterns and indicating
potentials for change.
Mobility patterns are expressed on a weekly basis and they refer to the average
kilometers traveled, the average traveling time and the average percentage of use
4Francesca Cellina et al.
Fig. 2.
The activities GoEco! tracked from the 359 members of the GoEco! community
during the first study period, from March 7,2016 to April 4,2016.
of each means of transport (aggregated into the following categories; car, public
transport, bicycle, walking, other). Details regarding total kilometers traveled,
traveling times, energy consumption and CO
2
per means of transport are also
provided.
Potential for change represents instead the possible mobility patterns a user
could have if she would always replace her trips with the most energy-efficient
mobility option. To identify them, an analysis with respect to several criteria is
necessary: we first distinguish systematic from non-systematic trips. For every
systematic trip, such as the daily commute to work, we identify a specific, path-
dependent alternative, while for non-systematic trips we simply consider general,
path-independent alternatives, only based on the length of the trip and the means
of transport used. Since users will seldom repeat non-systematic trips, regarding
them the report simply suggests aggregated potentials, obtained by general rules
such as: for trips shorter than 1kilometer, you could walk; for trips shorter than
3kilometers, you could use the bicycle, and so on.
We developed various tools to perform these analyses. For the distinction of
systematic and non-systematic routes, we employ a clustering algorithm that
detects important places for every user (cf. [
8
]), followed by an assessment of
which routes were taken how frequently. To determine the availability of energy-
efficient alternatives for systematic trips, we combine an expert system with a
custom route planner: while the route planner tries to find routes using various
GoEco! - Apps Supporting Sustainable Mobility 5
means of transport, the expert system determines when a journey should actually
be considered as a viable alternative.
4Behavior Change Motivation Mechanics
The key elements represented in the report are also summarized in the full GoEco!
app, so that users can always recall their baseline and potential values. Next to
such static feedback, the full GoEco! app uses a variety of interactive gamification
mechanics to nudge people towards more sustainable mobility patterns.
Since mobility choices are individual and depend on a variety of circumstances,
such as daily schedule, weather, or other persons involved, gamification elements
have to be chosen carefully [
10
]. Points, for example, are difficult to use, as a fair
distribution of points is a very delicate task in such a heterogeneous environment,
if one wants to respect all individual circumstances and stay transparent. For
example, users have different access to alternatives for car use, depending on the
places they live and work, or might have different family requirements influencing
their mobility needs. In general, there is no one-size-fits-all mobility solution to
be promoted by a super-imposed scoring system. Our use of gamification thus
revolves around personal goals for change with respect to the baseline mobility
patterns: users are invited to choose a personal goal towards sustainable mobility
patterns, selecting it from a list of possibilities (reduce car use, increase slow
mobility, reduce energy consumption, etc.), and also to set the quantitative target
they want to achieve. For this purpose, the App supports them, showing both
their “baseline” and “potential” mobility patterns (Figure 3).
Progression towards an own goal is therefore the key motivational factor,
both for the individual as well as for the social comparison to others. Users are
free to progress at their own pace and in their own direction, while being slightly
nudged by GoEco! to achieve their personal goal for change. To this purpose, we
use a system of additional motivation elements (Figure 4).
The first one is “information feedback”. When using feedback and gamification,
optimally, the feedback is given in a well-timed manner [
5
]. However, because
we rely on Moves for the activity tracking, this is difficult (the data Moves
provides gets updated at unknown points in time). As such, we encourage users
to interact with GoEco! once per day, at which point they receive feedback on
their daily activities. Progression towards their goals, instead, is shown on a
weekly basis, since daily goals would not be significant from the mobility point of
view: individual mobility demand might vary a lot from one day to another and
achievement of daily goals might simply be due to external factors conditioning
demand, not to a real change in the users’ mobility patterns.
Further motivational elements are “education”, “guidance” and “rewards”:
besides supporting users by indicating their potential for change and providing
them with personalized alternatives for systematic trips, GoEco! also guides users
by challenging them to adopt specific, sustainable mobility patterns and nudging
repetitions over time. Individual challenges compatible with the personal goal
chosen, such as “I will not use the car during peak hours for five days”, “I will
6Francesca Cellina et al.
Fig. 3.
Setting a personal goal, based on the assessment of GoEco!, which takes into
account the baseline data and a variety of alternative travel options.
not use either cars or planes for the whole week-end” or “I will travel by slow
mobility all my short routes”, are offered every week to the user, who is free to
choose the one(s) she prefers or to ignore them. Users who achieve challenges
are rewarded with a trophy. Successfully repeating challenges over time allows
leveling up and receiving higher-level trophies (bronze, silver, gold and platinum).
Users are also rewarded with surprise badges, which are automatically at-
tributed when specific sustainable mobility choices are detected by the system,
such as using the bicycle every day for at least five consecutive days or travelling
long trips by train.
The possibility of comparing one’s performances with the other members of the
GoEco! community (“social comparison”) is considered a powerful tool to increase
motivation for change [
1
]. Since we opted for avoiding a point-based system,
building a leaderboard is not straightforward. Coherently with the choice to put
personal goals for change at the center of our motivational mechanics, comparison
between members of the community is based on their level of achievement of their
own personal goals, combined with the number of challenges they completed and
the number of badges they obtained. The leaderboard is updated every week and
the top-3members are posted in the “Hall of fame” section (Figure 5).
Independent of whether the users choose simple or complex goals, they have
the chance to be listed in the GoEco! hall of fame if they achieved them. The
system doesn’t judge goal complexity, which depends on the users’ initial mobility
GoEco! - Apps Supporting Sustainable Mobility 7
Fig. 4.Components of the GoEco! motivation mechanics.
patterns, on the intensity of their potential for changes, on external, personal
constraints and on the overall level of engagement they accept.
Unexpected elements such as badges help for “commitment reinforcement”.
Besides this, GoEco! also promotes in-person meetings among members of the
community, named “collective challenges”. These are recreational events hinting
at sustainable mobility, such as slow mobility and public transport treasure hunts
across the city or lazy bicycle rides in natural areas, where participants can
meet, accompanied by their families. The virtual community is therefore backed
up by physical meetings held once a month, where participants can share their
experience and also try sustainable approaches to leisure time mobility (a further
guidance function).
Additional reinforcement is provided by a notification system, which remem-
bers users to validate their trips daily, notifies them of new challenges or updates
of the weekly statistics, and congratulates them whenever they achieve good
results (goals, challenges, badges, visibility in the hall of fame).
Finally, monthly quizzes and random draws with tangible prizes addressing
the active members of the community are used to further keep their commitment.
They are the only tangible motivational elements in the whole GoEco! experience.
By explicit choice, prizes have low monetary value. We want in fact to stimulate
mobility behavior change as a personal, intrinsic choice of the participants, instead
of buying their change for money, which would only have temporary effects.
8Francesca Cellina et al.
Fig. 5.The “Hall of fame” section in the GoEco! App.
5Conclusion and Outlook
The GoEco! Tracker App has been tested in the GoEco! living lab in Spring 2016.
An assessment of the baseline data yielded acceptable data and algorithm quality
[
3
]. We developed and presented algorithms to automatically analyze mobility
patterns and potentials of every user individually. The so generated reports are
used as a first feedback for users and are currently being integrated into the full
GoEco! App, to be deployed in Autumn 2016.
However, while the interest in GoEco! is generally high, about one third of
the participants, who voluntarily signed up for the study, did not even start with
the experiment. Also, only one third of them regularly validated the tracked trips.
Such a low activity rate raises doubts on the possibilities for future up-scaling and
enlargement of the GoEco! community at the society level. Before performing any
assessment, however, we’ll wait for the conclusion of the next phase of the GoEco!
living lab, when participants will test the full GoEco! App. Results of this activity
will show if the GoEco! approach is effective in improving mobility behavior and
also, up to some degree, which of the employed motivational elements had greater
effects on user behavior. If the overall approach will be proven effective, we will
then focus on how to favor a wide penetration of the GoEco! community within
society, directly involving participants to the living lab experiment, with the aim
of collecting their perceptions and suggestions.
GoEco! - Apps Supporting Sustainable Mobility 9
Acknowledgments
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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