Mobility as a Service (MaaS) is a recent innovative transport concept, anticipated to induce significant changes in the current transport practices. However, there is ambiguity surrounding the concept; it is uncertain what are the core characteristics of MaaS and in which way they can be addressed. Further, there is a lack of an assessment framework to classify their unique characteristics in a systematic manner, even though several MaaS schemes have been implemented around the world. In this study, we define this set of attributes through a literature review, which is then used to describe selected MaaS schemes and existing applications. We also examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices: travel demand modelling, a supply-side analysis, and designing business model. Finally, we propose the necessary enhancements needed to deliver such an innovative service like MaaS, by establishing the state of art in those fields.

Real-world data are key to the implementation and validation of urban transport models, and their availability and accuracy can dramatically affect the reliability of the resulting estimates. This paper discusses the potential of open data as a mean to gain insights in urban mobility, so as to supplement traditional methodologies that are often complex and expensive. We propose a methodology-fully based on publicly accessible data-for the development of Origin-Destination Matrices (ODMs). The methodology uses as input (i) a baseline morning-peak-hour ODM and (ii) road traffic count data. We test our proposed approach in a real-world case study, i.e., the city of Bologna, Italy. We also employ open geospatial data, from socioeconomic sources, to validate the ODMs, and find that these reproduce the typical urban travel demand profile in the target city during a whole day. Our results demonstrate the suitability of the methodology for transport policy design and urban planning. We also discuss current difficulties of gathering open data and the lessons learned when attempting to leverage such data.