Personalization and Recommendation Technologies for MaaS

K. Arnaoutaki, E. Bothos, B. Magoutas, G. Mentzas

Over the last few years, MaaS has been extensively studied and evolved into offering a multitude of mobility services that continuously increase, from alternative car or bike-sharing modes to autonomous vehicles, that aspire to become a part of this novel ecosystem. MaaS provides end-users with multimodal, integrated, and digital mobility solutions, including a multitude of different choices able to cover users specific needs in a personalized manner. This practically leads to a range of novel MaaS products, that may have complex structures and the challenge of matching them to user preferences and needs so that suitable products can be provided to end-users. Moreover, in the everyday use of MaaS, travelers require support to identify routes to reach their destination that adhere to their personal preferences and are aligned to the MaaS product they have purchased. This paper tackles these two user-centric challenges by exploiting state-of-the-art techniques from the field of Personalization and Recommendation systems and integrating them in MaaS platforms and route planning applications.

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