Presenter: Alessandro Ciociola
Monday, December 6th, 2021 17:30
Shared mobility is becoming increasingly popular in modern cities, as it is often reported to present several environmental, socio-economic, and transportation-related benefits. Developing methodologies to measure and evaluate the impacts of shared mobility has therefore become of critical importance for city authorities.With the increased integration between ICTs and transport systems, data collection opportunities are arising, as traditional survey-based methods are often costly, slow, and not enough to reflect the complexity and dynamicity of current mobility systems.In this context, we started the development of Odysseus, a software for data management, analysis, and simulation of shared mobility. Odysseus can collect and transform mobility-related (open) data and use them as input for demand/supply modelling and simulation of what-if scenarios. To show the workflow and potentialities of the software, we will present two recently published case studies focused respectively on car-sharing and e-scooter sharing.
Biography: Alessandro is a PhD student in Electrical, Electronics and Communications Engineering at Politecnico di Torino.
He got his Bachelor’s degree in Computer Engineering and Master’s degree in ICT for Smart Societies, both at Politecnico di Torino. In 2017, following the development of an Interdisciplinary Project within his Master’s degree, he co-authored his first paper and attended IEEE Smart Cities Innovation conference in San Francisco.
In December 2019, he joined SmartData@Polito as a Graduate Research Fellow. During the following year, he was involved in a research project with General Motors and co-authored three papers accepted at IEEE conferences.
Since November 2020, he is PhD student atSmartData@Polito under the supervision of Marco Mellia, Luca Vassio and Danilo Giordano.
His main research interest is applying Data Science techniques to study sustainable mobility and urban intelligence. In particular, he is using real world data to study serviceability, sustainability and scalability of different mobility solutions.