Promoting sustainable and personalized travel behaviors while preserving data privacy

Abstract

Cities worldwide have agreed on ambitious goals regarding carbon neutrality. To do so, policymakers seek ways to foster smarter and cleaner transportation solutions. However, citizens lack awareness of their carbon footprint and of greener mobility alternatives such as public transports. With this, three main challenges emerge: (i) increase users’ awareness regarding their carbon footprint, (ii) provide personalized recommendations and incentives for using sustainable transportation alternatives and, (iii) guarantee that any personal data collected from the user is kept private. This paper addresses these challenges by proposing a new methodology. Created under the FranchetAI project, the methodology combines federated Artificial Intelligence (AI) and Greenhouse Gas (GHG) estimation models to calculate the carbon footprint of users when choosing different transportation modes ( e.g. , foot, car, bus). Through a mobile application that keeps the privacy of users’ personal information, the project aims at providing detailed reports to inform citizens about their impact on the environment, and an incentive program to promote the usage of more sustainable mobility alternatives. • Novel methodology that promotes personalized and sustainable mobility behavior while preserving data privacy and increasing user trustworthiness. • New artificial intelligence and greenhouse gas estimation models to detect the type of transportation being used by a given citizen, along with its corresponding carbon footprint. • Federated learning solution, combined with differential privacy, that ensures the privacy of sensitive data being collected from users. • Compliance with European best practices in usability, accessibility, and explainable artificial intelligence to clarify in an understandable way how users’ data is being processed. • Proof-of-concept prototype, including a preliminary validation of its accuracy and main features.

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