H2020Индивидуална стипендия2017–2019

PRISM · PRobabilistic PRedictIon for Smart Mobility under stress scenarios

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2017-04-01 → 2019-03-31
Финансиране от ЕС: 212 195 €
Участници: 1
Схема: MSCA-IF-EF-RI

Линиите свързват координатора с партньорите.

Накратко на български

Интелигентният транспорт изследва прогнозирането на търсенето на превозни средства, например при споделените автомобили. Този подход помага за правилното разпределение на колите, за да не бъдат те празни там, където са необходими.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

PRISM is about designing, implementing and testing methodologies to better predict transport demand in a city. While plenty solutions exist today for this objective, there is general consensus that, under stress scenarios (e.g. large social events, inclement weather, demonstrations, special days), those approaches are insufficient.With new, smart mobility modes, demand prediction becomes even more 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 Computer Science, by using Probabilistic Graphical Models (PGMs), a tool that combines Bayesian statistics, graph theory and scientific computing. As a research area, PGMs have already reached a considerable level of solid foundations, community size, and software tools.The Experienced Researcher (ER) has recently returned to Europe, after several years of research in Singapore and USA with the Massachusetts Institute of Technology (MIT), and this Marie SkŁodowska-Curie fellowship will be instrumental for his growth and affirmation in the Danish and European context.

Оригинален текст от CORDIS (на английски).

Участници

DANMARKS TEKNISKE UNIVERSITET · Kongens LyngbyКоординаторДания