A Recommender System for Mobility-as-a-Service Plans Selection

Konstantina Arnaoutaki, Efthimios Bothos, Babis Magoutas, Attila Aba, Domokos Esztergár-Kiss and Gregoris Mentzas
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Konstantina Arnaoutaki: Institute of Computer and Communication Systems, National Technical University of Athens, 15780 Athens, Greece
Efthimios Bothos: Institute of Computer and Communication Systems, National Technical University of Athens, 15780 Athens, Greece
Babis Magoutas: Institute of Computer and Communication Systems, National Technical University of Athens, 15780 Athens, Greece
Attila Aba: Department of Transport Technology and Economics, Budapest University of Technology and Economics (BME), 1119 Budapest, Hungary
Domokos Esztergár-Kiss: Department of Transport Technology and Economics, Budapest University of Technology and Economics (BME), 1119 Budapest, Hungary
Gregoris Mentzas: Institute of Computer and Communication Systems, National Technical University of Athens, 15780 Athens, Greece

Sustainability, 2021, vol. 13, issue 15, 1-27

Abstract: Transportation and mobility in smart cities are undergoing a grave transformation as new ways of mobility are introduced to facilitate seamless traveling, addressing travelers’ needs in a personalized manner. A novel concept that has been recently introduced is Mobility-as-a-Service (MaaS), where mobility services are bundled in MaaS Plans and offered to end-users through a single digital platform. The present paper introduces a recommender system for MaaS Plans selection that supports travelers to select bundles of mobility services that fit their everyday transportation needs. The recommender filters out unsuitable plans and then ranks the remaining ones on the basis of their similarity to the users’ characteristics, habits and preferences. The recommendation approach is based on Constraint Satisfaction Problem (CSP) formalisms combined with cosine similarity techniques. The proposed method was evaluated in experimental settings and was further embedded in real-life pilot MaaS applications. The experimental results showed that the proposed approach provides lists of MaaS PlanMaaS Plans that users would choose in a real-life MaaS setting, in most of the cases. Moreover, the results of the real-life pilots showed that the majority of the participants chose an actual MaaS Plan from the top three places of the recommendation lists.

Keywords: mobility as a service; smart mobility ecosystems; smart cities; sustainable mobility; recommender systems; personalization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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