Towards improving personalisation of MaaS tools

Kamaldeep Singh Oberoi (1, 2) , Sébastien Parenty (3) , Eugénie Avril (4, 5) , Pratik Rai (6) , Valérie Camps (1, 2)

1 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
2 UT3 - Université Toulouse III - Paul Sabatier
3 ENAC - Ecole Nationale de l'Aviation Civile
4 CLLE - Cognition, langues, langage, ergonomie
5 UT2J - Université Toulouse - Jean Jaurès
6 DTIS, ONERA, Université de Toulouse [Toulouse]

Over the past few years, Mobility-as-a-Service (MaaS) has become an alternative to owning a private car, integrating various public and shared travel modes in a single platform. Despite its attractiveness in meeting everyday travel needs, it has not seen high penetration rates within the urban population. A solution to increase its acceptability is to improve its personalisation so that it can better cater to the needs of each user. In this paper, we propose the global architecture of a MaaS tool under development for the city of Toulouse and its surroundings. The proposed architecture combines the information about users' mobility patterns and their individual preferences along with real-time traffic updates to proactively recommend personalised routes. Within the application, users' probability of choosing a particular transport mode, calculated using discrete choice models, as well as various gamificationbased elements are integrated in order to understand users' travel behaviour and promote sustainable transportation.

Keywords: Mobility-as-a-Service; Personalisation; Gamification; Transport Mode Choice Modelling; Macroscopic Mathematical Traffic Modelling; Mobility Assistant

Domains: Artificial Intelligence [cs.AI]; Multiagent Systems [cs.MA]; Psychology; Fluids mechanics [physics.class-ph]

Kamaldeep Singh Oberoi, Sébastien Parenty, Eugénie Avril, Pratik Rai, Valérie Camps. Towards improving personalisation of MaaS tools. 14th ITS European Congress (2022), May 2022, Toulouse, France. pp.1-12. ⟨hal-03703695⟩