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PRISM

PRobabilistic PRedictIon for Smart Mobility under stress scenarios

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

PRISM project word cloud

Explore the words cloud of the PRISM project. It provides you a very rough idea of what is the project "PRISM" about.

fellowship sharing software pgms prism exist autonomous inclement context vehicles science operations instrumental prediction scientific solutions city service curie transport disastrous models consensus probabilistic scenarios stress massachusetts sensitive solid latest predict drivenow placing special combine graphical size foundations rebalancing supply affirmation tools tool theory odowska implementing insufficient danish engineering removing sk fact area weather marie bayesian days social mobility outcomes er car designing community statistics becomes reached considerable plenty mit smart singapore researcher graph returned computer computing predictions moving combines events bad modes usa demonstrations demand

Project "PRISM" data sheet

The following table provides information about the project.

Coordinator	DANMARKS TEKNISKE UNIVERSITET Organization address address: ANKER ENGELUNDSVEJ 1 BYGNING 101 A city: KGS LYNGBY postcode: 2800 website: www.dtu.dk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Denmark [DK]
Project website	http://mlsm.man.dtu.dk/research-projects/prism
Total cost	212˙194 €
EC max contribution	212˙194 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2016
Funding Scheme	MSCA-IF-EF-RI
Starting year	2017
Duration (year-month-day)	from 2017-04-01 to 2019-03-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	DANMARKS TEKNISKE UNIVERSITET DANMARKS TEKNISKE UNIVERSITET Organization address address: ANKER ENGELUNDSVEJ 1 BYGNING 101 A city: KGS LYNGBY postcode: 2800 website: www.dtu.dk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DK (KGS LYNGBY)	coordinator	212˙194.00

Map

Project objective

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 SkÅodowska-Curie fellowship will be instrumental for his growth and affirmation in the Danish and European context.

Publications

List of publications.
year	authors and title	journal	last update
2019	Filipe Rodrigues, Ioulia Markou, Francisco C. Pereira Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach published pages: 120-129, ISSN: 1566-2535, DOI: 10.1016/j.inffus.2018.07.007	Information Fusion 49	2019-09-02
2018	Rodrigues, F., & Pereira, F. C. Deep Learning from Crowds published pages: 1611-1818, ISSN: , DOI:	The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)	2019-09-02
2018	F Rodrigues, K Henrickson, FC Pereira Multi-output Gaussian processes for crowdsourced traffic data imputation published pages: 1-10, ISSN: 1524-9050, DOI:	IEEE Transactions on Intelligent Transportation Systems	2019-09-02
2018	Filipe Rodrigues, Francisco C. Pereira Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data published pages: 636-651, ISSN: 0968-090X, DOI: 10.1016/j.trc.2018.08.007	Transportation Research Part C: Emerging Technologies 95	2019-09-02

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The information about "PRISM" are provided by the European Opendata Portal: CORDIS opendata.