GoEco! - Fostering Sustainable Mobility

The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, public transportation and soft mobility would be able to effectively satisfy many travel needs. However, they tend to be neglected, due to a deep-rooted car dependency. How can we encourage people to make sustainable mobility choices, reducing car use and the related CO_2 emissions and energy consumption? Taking advantage of the wide availability of smartphone devices, we designed GoEco!, a smartphone application exploiting automatic mobility tracking, eco-feedback, social comparison and gamification elements to persuade individual modal change. We tested the effectiveness of GoEco! in two regions of Switzerland (Cantons Ticino and Zurich), in a large-scale, one year long randomized controlled trial. Notwithstanding a large drop-out rate experienced throughout the experiment, GoEco! was observed to produce a statistically significant impact (a decrease in CO_2 emissions and energy consumption per kilometer) for systematic routes in highly car-dependent urban areas, such as the Canton Ticino. In Zurich, instead, where high quality public transport is already available, no statistically significant effects were found. In this paper we present the GoEco! experiment and discuss its results and the lessons learnt, highlighting practical difficulties in performing randomized controlled trials in the field of mobility and providing recommendations for future research.

How can we encourage people to make sustainable mobility choices, reducing car dependency and the related CO 2 emissions and energy consumption? Taking advantage of the wide availability of smartphone devices, we designed GoEco!, an application (app) exploiting automatic mobility tracking, eco-feedback, social comparison and gamification elements to persuade individuals to change their mode of transport. The app features and contents are grounded in the transtheoretical model for behaviour change and were designed to avoid over-reliance on "one-size-fits-all", simplistic point-based systems. The GoEco! app was designed in a user centred approach and was field-tested in Switzerland in a three-month experiment involving around 150 voluntary users. In this paper, we present the app's features and comment on their evaluation from the perspective of the field-testers. The insights we collected through an online questionnaire and individual interviews allowed us to develop recommendations for similar persuasive apps and to identify open challenges for the future. In particular, we recommend to endow such apps with multi-modal travel planning components and features evoking the feeling of belonging to a community, that provide support and helping relationships.

Nowadays, most people carry around a powerful smartphone which is well suited to constantly monitor the location and sometimes even the activity of its user. This makes tracking prevalent and leads to a large number of projects concerned with trajectory data. One area of particular interest is transport and mobility, where data is important for urban planning and smart city-related activities, but can also be used to provide individual users with feedback and suggestions for personal behavior change. As part of a large-scale study based in Switzerland, we use activity tracking data to provide people with eco-feedback on their own mobility patterns and stimulate them to adopt more energy-efficient mobility choices. In this paper we explore the opportunities offered by smartphone based activity tracking, propose a general framework to exploit location data to foster more sustainable mobility behavior, describe the technical solutions chosen and discuss a range of outcomes in terms of user perception and sustainability potential. The presented approach extracts mobility patterns from users’ trajectories, computes credible alternative transport options, and presents the results in a concise and clear way. The resulting eco-feedback helps people to understand their mobility choices, discover the most non-ecological parts of their travel behavior, and explore feasible alternatives.

The diffusion of electric vehicles is currently the most promising opportunity to reduce the dependency on fossil fuel in the mobility sector. However, the adoption of the electric vehicles seems to be still hindered by psychological and behavioural barriers. Thus, in order to understand and to foster the transition towards more sustainable mobility styles, it becomes essential to adopt an inter-disciplinary approach. In this framework, the e-mobiliTI project was launched in late 2012 in Southern Switzerland. It aims at understanding the potential for transition in the mobility system at the local level, with a special focus on electric mobility. The project builds upon a small living lab made up of around twenty families, who will be monitored in all their trips through smart mobile devices, in order to get quantitative data, and through focus groups, in order to get qualitative data and perceptions. Here we discuss the major challenge in the initial stage of the e-mobiliTI project, that is the gathering of reliable and high-quality data on users'behaviour. We describe the automatic tracking system, and the data processing and the qualitative assessment approach and comment on the overall performances of the living lab experiment.

Diffusion of smart mobile devices offers unprecedented opportunities to monitor travel behaviour, by means of the GPS devices they are equipped with: using a suitable application, potentially every smartphone owner can produce huge, inexpensive quantities of data suitable to profile her mobility patterns. We take advantage of this opportunity within the e-mobiliTI project, which aims at analysing the main psychological and behavioural barriers affecting the transition to new mobility solutions. The project sets up a ”living lab” made up of around twenty families and gives them the opportunity to test electric cars and bikes, public transport season tickets and car and bike sharing. Beside traditional social research tools (questionnaires, interviews, focus groups), to analyse their mobility behaviour we use a specifically developed smartphone application. In this paper we present the results of the first phase of our field trial and discuss the major challenges faced so far in the automatic gathering of mobility data: high battery consumption, limited performances of the GPS smartphone devices, problems in the Internet connectivity, limited reliability of the information the application asks to the users and risk that the users quit using the application, due to the lack of immediate compensation for the nuisance of being always monitored and for the daily effort of actively using the application.

The diffusion of electric vehicles is one of the most promising opportunities to reduce dependency on fossil fuels and to pave the way for the transition to a more sustainable mobility. However, apart for the main barrier still represented by the purchase cost, the adoption of electric vehicles is still hindered by other barriers, such as autonomy, recharge time any general performance. Therefore, fostering a change in the present mobility patterns requires to go beyond the traditional technological approach and to explicitly address consumers perceptions and behaviour. In 2012 we launched the e-mobiliTI project to get a deeper understanding of the factors favouring or opposing the transition to e-mobility. This project builds upon the living lab approach, focusing on a small sample of families located in Southern Switzerland. Family members accepted to be monitored in all their trips, in exchange for the availability, for a period of three months, of electric cars and bicycles, public transport seasonal tickets and car and bike-sharing subscriptions. In Spring 2013 a first three-months monitoring phase allowed us to identify their present mobility patterns and styles, while in Spring 2014, during a second three-months monitoring phase, the participants experienced the new mobility options in real-world settings. In order to monitor travel behaviour, we relied on both quantitative automatic data-gathering techniques and on qualitative focus groups and interviews. Automatic data-gathering was performed thanks to a specifically developed smartphone application that relied on GPS tracks. To identify the significant variations of mobility patterns between the two monitoring phases, we developed a data mining approach based on regression trees. In this paper we present the results of the e-mobiliTI project and conclude with a critical analysis of our approach, especially regarding the problems in automatic data gathering and mobility profiling and the limited representativeness of our results, due to the small size of our sample and the short duration of the testing period.

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.

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.

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.

Sustainable human-computer interaction is investigating the role of persuasive and gamified technologies in encouraging people to engage in a more sustainable lifestyle. Motivation is a key requirement for behavior change, yet many persuasive systems do not sufficiently account for motivational aspects. In this paper we investigate under which circumstances components such as feedback and game elements (e.g., rewards) afford user motivation. The result is a taxonomy of design components that is grounded in well-established psychological theories on motivation. We illustrate how the taxonomy can contribute to the design of meaningful persuasive technologies by discussing a case study from the domain of sustainable mobility behavior (the project GoEco!).

Poster presented at the 2015 SCCER Mobility annual conference

The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, alternative and effective transport modes are already available and they could be used in inter‐modal combinations to satisfy many travel needs: public transportation, slow mobility networks, vehicle‐sharing systems. However, these transport modes still tend to be neglected due to a deep‐rooted car dependency. How can we encourage people to engage in more sustainable mobility lifestyles, reducing use of the car? With the Switzerland‐based project GoEco! we seek to overcome the traditional awareness‐raising approach and develop a smartphone application (app) that leverages eco‐feedback information, social norms and peer pressure, adopting a “gamification” approach. The project is funded by the Swiss National Science Foundation – NRP71 and by the Swiss Competence Centre on Energy Research SCCER‐Mobility.

How can we encourage people to engage in more sustainable mobility lifestyles, reducing car use? Taking advantage of the wide availability of smartphones, we overcome the traditional awareness-raising approach and exploit eco-feedback, social norms and peer pressure elements 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. Exploiting 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.

Current popular multi-modal routing systems often do not move beyond combining regularly scheduled public transportation with walking, cycling or car driving. Seldom included are other travel options such as carpooling, carsharing, or bikesharing, as well as the possibility to compute personalized results tailored to the specific needs and preferences of the individual user. Partially, this is due to the fact that the inclusion of various modes of transportation and user requirements quickly leads to complex, semantically enriched graph structures, which to a certain degree impede downstream procedures such as dynamic graph updates or route queries. In this paper, we aim to reduce the computational effort and specification complexity of personalized multi-modal routing by use of a preceding heuristic, which, based on information stored in a user profile, derives a set of feasible candidate travel options, which can then be evaluated by a traditional routing algorithm. We demonstrate the applicability of the proposed system with two practical examples.

The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.

The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, alternative and effective transport modes are already available, ranging from well-established systems such as public transportation and slow mobility networks to emerging alternatives like vehicle-sharing systems. However, these transport modes still tend to be neglected due to a deep-rooted car dependency. How can we encourage people to use them? In the Swiss-based GoEco! project we overcome the traditional awareness-raising approach. We develop a set of two smartphone Apps leveraging eco-feedback and game elements and create a medium to large-scale “living lab” experiment to test their effectiveness in motivating people to modify their mobility behaviour. The GoEco! living lab is developed in two contexts differing both in the supply of mobility options and in the socio-cultural attitude of the population towards mobility: the City of Zurich and the Canton Ticino region. The experiment envisions three mobility tracking periods: the first one to identify the reference mobility patterns, the second one to identify the nudged mobility patterns, under the direct effect of the GoEco! App, and the last one, one year later, to assess long‐term behaviour change towards less car-dependant mobility styles. Focus groups and semi‐structured interviews with randomly selected participants will provide us with additional qualitative insight on the users' perceptions and attitudes. After an introduction the GoEco! living lab experiment and methodological approach, we present preliminary insights on the data collected during the first mobility tracking period.

The project GoEco! takes advantage of the wide availability of smartphones, in order to overcome the traditional awareness-raising approach used to foster sustainable mobility and exploit eco-feedback, social norms and peer pressure elements in an ICT-based motivation system. In particular, it uses a smartphone app to analyze how we can encourage people to engage in more sustainable mobility lifestyles. This poster discusses the various challenges we faced when deploying GoEco! Tracker (an app using the Moves® fitness tracker to collect mobility measurements), and provides a summary of results obtained by one month of large scale testing within the GoEco! living lab performed in Switzerland, allowing us to collect baseline mobility data for the sample of voluntary participants of the GoEco! living lab.