Fig 3 - uploaded by Francesca Cellina
Content may be subject to copyright.
Setting a personal goal, based on the assessment of GoEco!, which takes into account the baseline data and a variety of alternative travel options.
Source publication
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, whic...
Context in source publication
Context 1
... 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). ...
Similar publications
La creciente concienciación social respecto a la construcción sostenible y el ahorro energético en edificios está conduciendo a prescriptores y usuarios finales a tomar en consideración nuevas soluciones constructivas. Sin embargo, su introducción en el mercado es lenta y difícil, debido tanto a las limitaciones de las herramientas reconocidas de s...
A Borehole Thermal Energy Storage living lab was built up nearby Torino (Northern Italy). This living lab aims at testing the ability of the alluvial deposits of the north-western Po Plain to store the thermal energy collected by solar thermal panels and the efficiency of energy storage systems in this climatic context. Different monitoring approac...
One-to-one computing has been proposed as one solution for improving primary school education around the world. Ukombozi School is a public primary school in Mkimbizi village, Tanzania, and the school has performed outstandingly on the district, regional, and national levels. Ukombozi school has 100 XO-1 "children's laptops", and, in collaboration...
The progressive paradigm shift from the centralized generation of electricity towards decarbonized and distributed energy systems is deeply changing the way energy is produced, distributed, and consumed. A relevant attention is currently being paid to the development of innovative control strategies for the management of the increasing complexity o...
Innovation serves many purposes. In this paper we study new varieties of innovation and innovation policy which address societal challenges in the largest cities in Europe. These metropolitan areas consistently show resounding characteristics in terms of multiplicities of innovation, governance and societal challenges. They serve as 'living labs' a...
Citations
... In the Swiss-based GoEco! project 1 [10], we addressed these issues, taking advantage of the wide availability of smartphone devices to encourage people to reduce their use of cars. Using a specifically developed smartphone app [7,9], we performed a large scale field test, monitoring the activities of several hundred volunteer citizens from Southern Switzerland and the City of Zurich. During three distinct mobility tracking periods distributed over a year, each one lasting at least six weeks, the movements and transport mode choices of participants were recorded. ...
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 GoEco! project [56] assessed whether gamified smartphone apps (containing playful elements, such as point schemes, leaderboards, or challenges; cf. [57,58]) are able to influence the mobility behavior of people. ...
For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach), and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models.
... We argue that eco-feedback can be improved by taking into account peculiarities of individual mobility and propose a two-step approach where we first identify users' individual mobility patterns (i.e., a user's "systematic mobility") and then compute meaningful and sustainable travel alternatives (a user's "potential for change"). We deployed this approach in the Swiss-based GoEco! project [2], which uses a gamified smartphone app to influence the mobility behavior of 213 volunteer users over the course of 4.5 months (cf. [1]). ...
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.
Travel surveys and other traditional methods have been used for collecting mobility data since 1930s. Those surveys have been so far the most reliable approaches to understand people mobility patterns, but their high costs do not allow a high frequency collection to obtain continuously updated data. To overcome these limitations, digitalization opens the gate for renewed travel data collection and analysis methods. To this extent, this paper aims to present a review of the various smartphone applications, classifying them according to three different purposes: 1) Travel Data Collection and Analysis; 2) Travel Surveys; and 3) Promotion of Sustainable Mobility. 81 apps were retrieved and analysed in detail and evaluated according to their features and the methods used for data collection. A subsequent SWOT analysis has then been performed to understand the strengths, weaknesses, opportunities and threats of using the smartphone applications to understand mobility patterns. Finally, recommendations for future research are put forward.
