From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. (January 2019)

• Record Type: Journal Article
• Authors: Bucher, Dominik; Mangili, Francesca; Cellina, Francesca; Bonesana, Claudio; Jonietz, David; Raubal, Martin
• Is Part Of: Travel behaviour and society. Volume 14(2019)
• Page Start: 43
• Page End: 56
• Publication Date: 2019-01
• Subjects: Mobility tracking -- Mode detection -- Trajectory clustering -- Eco-feedback -- Sustainability; Transportation -- Periodicals; Population geography -- Periodicals; 303.48305
• DOI: 10.1016/j.tbs.2018.09.005
• Languages: English
• ISSNs: 2214-367X

Abstract

Highlights: We present and validate a method to extract systematic mobility. Based on context, our method evaluates alternative travel options. A large user survey confirms the applicability of our method. Systematic car journeys offer a large potential to increase sustainability. These journeys are often traveled alone, making bicycles great alternatives. Abstract: 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.