Abstract
University campuses concentrate people movement within a city, being places of study, work, research, and co-presence. In that aspect, the university community travel patterns must be addressed through Commute Management Plans (CMPs) which provide guidelines to policymakers’ actions which envision a transition towards sustainable mobility alternatives. CMPs are accessories to Sustainable Urban Mobility Plans (SUMPs), focused on analysing supply and demand for transport, the university staff movement patterns, and the origin-destination dynamics between their households and workplaces. Moreover, besides the staff-oriented evaluation, universities must also consider the students’ commute patterns, as those comprise a significative part of their community. While in most cases, university structures are placed in few campuses, with movement concentration towards those areas and within them, many Italian Universities, such as the University of Pisa, present a distinct spatial organization, as its various structures are parcellated and diffused within the city area; a paradigmatic case of a “city-university” where moving in-between and within the university sites is moving within the city itself. Such characteristics require a data-driven approach that integrates spatial, configurational, and transportation analyses. Considering this, the paper discusses computational methods used to elaborate the University of Pisa’s CMP and how a data-driven approach that integrates multi-domain analyses can highlight the movement patterns in a “city-university” context. The discussions and findings can serve as examples to other CMPs for universities within similar contexts – important, given their requirement by law in several European countries – or even as approaches to be incorporated in urban mobility and city master plans.
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Altafini, D., Mara, F., Cutini, V. (2023). A Data-Driven Approach for a City-University Mobility Plan: The Case of the University of Pisa. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14111. Springer, Cham. https://doi.org/10.1007/978-3-031-37126-4_27
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