As mobility planning goes hand in hand with urban planning, this new mobility planning is part of a possible new configuration of urban centres, which in the scientific literature is called the “15-minute city”. Such a new layout is, in fact, possible thanks to the widespread use of the bicycle as a means of both short trips and last-mile journeys.
Specifically, with regard to last-mile mobility, Poliedra has, in the past, carried out a project aimed precisely at improving bike-train-bike intermodal mobility (BiTiBi project), which led to the construction of several velostations in some railway stations on the suburban lines connecting Milan with its suburbs and surrounding municipalities.
3. In which ways is the nuMIDAS toolkit going to contribute to research institutions?
The nuMIDAS toolkit will be very important for the possibility of creating scenarios that are as close as possible to the local reality to which they apply. In this way, the results will be more relevant and more precise, providing a useful tool for analysing the local mobility system and visualising the possible future effects of policies on urban mobility.
This would allow researchers to study and compare different scenarios by analysing how the final outputs can vary as the initial conditions vary and what their consequences are on the transport system of the city.
In addition, many research institutes, including Poliedra, which provide technical assistance to local authorities, can use a tool that returns outputs that can support decisions on mobility issues such as parking management, fleet sizing, implementation of low emission zones, etc.
In conclusion, the toolkit, thanks to its visualisation in a dashboard, allows to deepen the analysis on mobility, resulting, consequently, a useful tool for decision support.
4. As a research institution, what do you see as the biggest challenges in the coming years with regard to the use of mobility data?
In the near future, thanks to the spread of Mobility as a Service (MaaS), there will be a need to manage and analyse data especially in real time in order to provide more and more up-to-date information to users. In this way, the user has the possibility to choose the best solution according to the current mobility situation. In addition, real-time data analysis can help public transport companies manage their fleet during peak times, providing more vehicles to meet momentary demand. Finally, data analysis can provide a much more detailed view of mobility in both time and space. In this way, origin/destination matrices can be more accurate, providing valuable information such as demand peaks to public decision makers, public transport managers, sharing service providers as well as transport and mobility researchers.