Explore the words cloud of the SOM project. It provides you a very rough idea of what is the project "SOM" about.
The following table provides information about the project.
INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE
|Coordinator Country||France [FR]|
|Total cost||149˙109 €|
|EC max contribution||149˙109 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2016-10-01 to 2018-03-31|
Take a look of project's partnership.
|1||INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE||FR (LE CHESNAY CEDEX)||coordinator||149˙109.00|
This project will commercialize software and internet services for improving mobility-related online services such a ride sharing, navigation systems and crowdsourcing. In particular, we will commercialize an app for mobile devices which makes predictions on the future travel intentions of the user based on the rich data available on the device through the use of highly efficient predictive modeling techniques. Based on these predictions, the mobile device can communicate in the background on ride sharing, crowdsourcing and congestion avoidance. Three key competitive advantages are (1) the privacy one gets in contrast with most cloud-based platforms where all data is centrally collected, (2) the saving of computational costs by distributing computations to devices having much more detailed information than what one can centrally collect, and (3) the fact that most services work with current traffic information, while we work with future travel intentions, allowing for planning better ahead. The commercialization of this technology will be realized by improving the existing network of potential industrial partners and end-users, demonstrating the technology for key services, exploring alternative commercialization strategies, and preparing a team to carry on the exploitation. When successful, this project will have lead to significant economic and societal benefits in improving the efficiency of crowdsourcing and ride sharing, increasing the popularity of ride sharing and reducing congestions.
|year||authors and title||journal||last update|
Exploiting traffic data
published pages: , ISSN: , DOI:
Invited talk: Learning from partly observable time- dependent graphs
published pages: , ISSN: , DOI:
|ECML/PKDD 2017 workshop on large scale time dependent graphs||2019-06-13|
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The information about "SOM" are provided by the European Opendata Portal: CORDIS opendata.
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