PRISM · PRobabilistic PRedictIon for Smart Mobility under stress scenarios

PRobabilistic PRedictIon for Smart Mobility under stress scenarios

With new, smart mobility modes, demand and supply prediction becomes truly important. For example, a one-way car sharing (e.g. DriveNow) or an autonomous mobility on demand service, are highly sensitive to rebalancing operations (moving vehicles to where demand is expected). In fact, bad demand predictions can lead to disastrous outcomes, by placing supply where it is not needed, and removing it from where it is required. PRISM approach is to combine latest research from Transport Engineering and Machine Learning, by using Probabilistic Graphical Models (PGMs) and Deep Neural Networks, two complementary tools that involve Bayesian statistics, graph theory, and optimization. As a research area, PGMs and DNNs have already reached a considerable level of solid foundations, community size, and software tools. And PRISM extended this impact to the field of transportation. The research behind the project has led to 12 journal publications, 2 (completed) and 4 (ongoing) PhD students, and other international recognition achievements, including journal editorial boards, keynote speeches, one new book edition, and 5 international PhD co-supervisions. The Experienced Researcher (ER) returned to Europe in 2015, after several years of research in Singapore and USA with the Massachusetts Institute of Technology (MIT), and this Marie SkŁodowska-Curie fellowship (2017-2019) was instrumental for his growth and affirmation in the Danish and European context. In fact, PRISM was the backbone for the establishment and growth of the Machine Learning for Smart Mobility group (http://MLSM.man.dtu.dk).

Data: CORDIS, © European Union