IMPReSS: A comprehensive method to classify MaaS systems

Abstract

Mobility as a Service (MaaS) has the potential to improve urban mobility by reducing car ownership and promoting multimodal transport through a single mobility app that provides trip planners, booking, ticketing, and payment for different modes and bundles. However, not all existing MaaS (or potential MaaS) systems provide a fully integrated system. Several authors have proposed different methodologies to assess MaaS integration. However, these approaches have some limitations regarding the scalability or characterization of the systems, making it hard to compare between them, as the current approaches are all incremental. In this work, we propose a new method called IMPReSS, a binary coding approach that assesses several dimensions of MaaS: Information, Multimodality, Payment, Reservation, Subscription, and Societal goals. It allows proper classification and comparison of different systems, using the societal goals as a cornerstone for its evaluation. Also, we propose a complementary scoring system based on the IMPReSS topology. This qualitative and quantitative methodology will allow practitioners and researchers to characterize, identify, and adequately assess transportation services and their potential to become a fully integrated MaaS system. Our results show that mature MaaS systems with public authorities involved, and some often excluded from MaaS discussion systems are ranked higher, while some wrongly called MaaS apps are ranked at the bottom. In summary, it assesses systems from a different and flexible perspective.