Shared practices and experiences of University Mobility Management. Exploring scalability at the municipal level

Mobility management (MM) gained importance to improve urban transport flows’ efficiency, aiming at reducing private mobility’s externalities such as congestion, emissions and land consumption. The focus is on encouraging citizens to develop more sustainable travel habits, considering that commuting generates the most significant impacts. For this reason, MM strategies prove beneficial when shared between companies operating in the same area. Such collaboration is rare due to the specific features of each company, with data collection for mobility planning emerging as one of the most critical challenges. Universities are a particular case because they daily attract both: students and workers. Italian universities are collaborating to achieve common practices in MM. One of these actions consists in the elaboration of a common questionnaire for the survey of mobility habits, to collect a set of data comparable between different kind of institutions and urban contexts, and to share best practices in MM policies and interventions. University communities address these issues as students are directly affected by urban transport and are often interested in developing sustainable practices. This contribution explores how they can be a source of good MM practices that can be transferred and shared with companies and institutions in the same area. In particular Mobility as a Service (MaaS) would be investigated. When talking about data collection and changing mobility habits, MaaS emerges as a useful tool for both goals. Experiences within academic communities could help to better design the service based on users’ needs for example by considering which mobility services should be included, balancing costs and incentives, and assessing the acceptance of participants to systematically share their travel information. This can lead to more consistent data collection, resulting in more efficient management of flows and better targeting of future interventions.