CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.
Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .
Livrables
The document will summarize the best project proposals, following the adopted business generation process. For the most promising ideas, a development plan will be detailed further.
Project GO-TO-Market ResultsThis deliverable will describe the go to market results comparing planned vs closed actions , discussing target achievement and analysing success factors and critical issues. Specific recommendations will be given to improve market penetration, tuning business approaches to sectors.
Mid-term evaluation report for Data Management Platform including PIAThis deliverable will present the results of execution of preliminary suite of functionality, performance and scalability tests which address the technical efficacy of the Data Management Platform. This report will be used to address specific improvement actions in second development phase. This deliverable will also provide an updated Privacy Impact Assessments (PIA).
Take up monitoring 2This deliverable will be an update of the previous with the aim of providing an objective evaluation of the take up rate of success. Some hints will be given for further improvements and correction.
Exploring prediction perspectivesThis deliverable will provide a review of the state-of-the-art of prediction methodologies in the mobility domain. Special focus will be given to data availability issues and machine learning techniques.
First evaluation of Visual Analytics and Decision Support systemThis deliverable will outline the methodology and the results of the evaluations and revise the requirements based on evaluation feedback.
Final evaluation of predictors for smart mobilityThe deliverable will follow up on D4.3 and conduct the final round of assessment of the metrics put forward in D4.4 based on showcase data, and detail how selection may be based on goodness to fit and complexity.
Security requirements for the SETA technologyThis deliverable will be dedicated to the security requirements for SETA technology (architecture, data and applications) providing a reference basis for the overall project development.
Risk Management PlanThis deliverable will cover both internally (related to individual participants) and externally induced risks. The risk management plan is constantly updated.
Final Evaluation of decision support systemThis deliverable will outline the methodology and the results of the evaluations and provide future recommendations based on evaluation feedback.
Initial evaluation of predictors for smart mobilityThis deliverable will document the performance of the metrics put forward earlier in WP4 in terms of limitations, accuracy, robustness and computation time.
Preliminary requirements for Visual Analytics, Data exploration and Decision Support systemThis deliverable will outline the requirements for the development of Visual Analytics, Exploration and Decision Support tools applied to mobility and big data.
Use Case Requirement AnalysisThis deliverable will outline the methodology and the results of the user requirements analysis process in the case studies.
Final evaluation report for Data Management Platform including PIAThis deliverable will be an update of D6.5 with the aim of providing an objective evaluation of technical efficacy of the Data Management Platform. The report will present the execution results of final suite of functionality, performance and scalability tests (including suggestions for further improvements and development of the Platform in commercial use), as well as the final Privacy Impact Assessments (PIA).
Take up monitoring 1This deliverable will provide a description of each KPI selected as a measure of take up: motivation, definition, evaluation rule. The KPIs values will be computed, when applicable, and shown in a dedicated session of the report. This report will be used to address specific improvement actions.
Progress Monitoring and Quality Management PlanThis deliverable will report on the guidelines and communication and reporting platform employed in order to facilitate the work of the project management for all participants, increase efficiency and reduce project overheads.
In this deliverable, the demand and supply predictors developed in D4.2 will be further enhanced by embedding them in Big Data streams and the implementation of online learning techniques to allow the prediction models to evolve and capture changing patterns.
Case Study Design and Development V2 (TOR)The document will update the description of Turin case study and its deployment provided in version 1, to the real deployments done in order to record modifications occurred during project development.
Final methodologies and tools for Visual Analytics and Decision Support systemThis deliverable will demonstrate the final version of the methodologies and tools for Visual Analytics and Decision Support applied to mobility, taking into account the revised requirements.
Case Study Design and Development V2 (STA)This deliverable presents the new design and implementation, incorporating on the one hand the conclusions of the evaluation process of the proposals and on the other hand the technologies offered by other work packages of SETA.
Case Study Design and Development V1 (STA)This deliverable will collect all aspects of the design phase of the solutions to be implemented in the case study. This includes aspects related to the specific description of measures to be implemented, as the design itself and the final implementation process.
Initial methodologies and tools for Visual Analytics and Decision Support systemThis deliverable will demonstrate the first version of the methodologies and tools for Visual Analytics and Decision Support applied to mobility.
Case Study Design and Development V1 (TOR)The document will provide a detailed description of Turin case study and its deployment. Turin case study and deployment will be defined keeping in mind Turin mobilities needs, already existing infrastructures and info services and Seta new technologies.
Initial modelling of non-vehicular transportThis deliverable will review the current state-of-the art, and pilot and validate the tools required to collect robust non-vehicular transport data (including environmental and behavioural factors). Initial data will be collected from limited settings using smartphones, GPS bikes, other sensors and interviews.
Final modelling of non-vehicular transportThis deliverable will present and evaluate predictive models of urban mobility, tested against observed journeys in multiple settings collected using the methods piloted in D.4.7.
Real-time simulation models for smart mobilityIncorporation of models and indicators under investigation in other WP4 tasks into a simulation environment of the test areas and demonstrated.
Case Study Design and Development V2 (BIR)The second case study will be focused on analysis and findings from the data modelling, testing the benefits of using the technology, methodology and tools that have been developed from the initial case study. Services will be developed and deployed to citizens, decision makers and business.
Case Study Design and Development V1 (BIR)Birmingham will make available local data such as traffic sensors, GPS enabled bikes etc… in conjunction with FLOOW data and implementation of Sheffield Hallam passive tracking devices. The initial case study design and development is to generate access to existing data and generate new data.
Initial development of demand and supply predictorsThis deliverable will present the development of supply and demand prediction building blocks. Based on the methods reviewed in D4.1, simulation model-based and data-driven methods will be formulated to estimate aggregate and dis-aggregate demand levels as well as real-time supply restrictions.
Publications
Auteurs:
Clélia Lopez, Ludovic Leclercq, Panchamy Krishnakumari, Nicolas Chiabaut, Hans van Lint
Publié dans:
Scientific Reports, Numéro 7/1, 2017, ISSN 2045-2322
Éditeur:
Nature Publishing Group
DOI:
10.1038/s41598-017-14237-8
Auteurs:
Panchamy Krishnakumari, Tin Nguyen, Léonie Heydenrijk-Ottens, Hai L. Vu, Hans van Lint
Publié dans:
Transportation Research Record: Journal of the Transportation Research Board, Numéro 2645/1, 2017, Page(s) 94-103, ISSN 0361-1981
Éditeur:
US National Research Council
DOI:
10.3141/2645-11
Auteurs:
Clélia Lopez, Panchamy Krishnakumari, Ludovic Leclercq, Nicolas Chiabaut, Hans van Lint
Publié dans:
Transportation Research Record: Journal of the Transportation Research Board, Numéro 2623/1, 2017, Page(s) 98-107, ISSN 0361-1981
Éditeur:
US National Research Council
DOI:
10.3141/2623-11
Auteurs:
Allan De Freitas, Lyudmila Mihaylova, Amadou Gning, Marek Schikora, Martin Ulmke, Donka Angelova, Wolfgang Koch
Publié dans:
IEEE Transactions on Aerospace and Electronic Systems, 2018, Page(s) 1-1, ISSN 0018-9251
Éditeur:
Institute of Electrical and Electronics Engineers
DOI:
10.1109/TAES.2018.2874147
Auteurs:
Hayder M. Amer, Hayder Al-Kashoash, Matthew Hawes, Moumena Chaqfeh, Andrew Kemp, Lyudmila Mihaylova
Publié dans:
Technological Forecasting and Social Change, Numéro 142, 2019, Page(s) 235-248, ISSN 0040-1625
Éditeur:
Elsevier BV
DOI:
10.1016/j.techfore.2018.09.013
Auteurs:
Suvodeep Mazumdar, Stuart Wrigley, Fabio Ciravegna
Publié dans:
Remote Sensing, Numéro 9/1, 2017, Page(s) 87, ISSN 2072-4292
Éditeur:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/rs9010087
Auteurs:
Ding Luo, Oded Cats, Hans van Lint
Publié dans:
Transportation Research Record: Journal of the Transportation Research Board, Numéro 2652, 2017, Page(s) 39-49, ISSN 0361-1981
Éditeur:
US National Research Council
DOI:
10.3141/2652-05
Auteurs:
Hayder, A.; Salman, N; Hawes, M.; Chaqfeh, M.; Mihaylova, L.S.; Mayfield, M.
Publié dans:
1424-8220, Numéro 1, 2016, ISSN 1424-8220
Éditeur:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/s16071013
Auteurs:
Mazumdar, Suvodeep; Zhang, Ziqi
Publié dans:
LD4IE 2016 : Linked data for information extraction : Proceedings of the Fourth International Workshop on Linked Data for Information Extraction co-located with 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 18, 2016., Numéro Vol 1699, 2016, Page(s) 2-10
Éditeur:
CEUR Workshop Proceedings
Auteurs:
Ding Luo, Oded Cats, Hans van Lint
Publié dans:
2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2017, Page(s) 744-749, ISBN 978-1-5090-6484-7
Éditeur:
IEEE
DOI:
10.1109/MTITS.2017.8005611
Auteurs:
Waqas Aftab, Allan De Freitas, Mahnaz Arvaneh, Lyudmila Mihaylova
Publié dans:
2018 21st International Conference on Information Fusion (FUSION), 2018, Page(s) 1-8, ISBN 978-0-9964527-6-2
Éditeur:
IEEE
DOI:
10.23919/ICIF.2018.8455501
Auteurs:
Kennedy J. Offor, Matthew Hawes, Lyudmila Mihaylova
Publié dans:
2018 21st International Conference on Information Fusion (FUSION), 2018, Page(s) 1185-1192, ISBN 978-0-9964527-6-2
Éditeur:
IEEE
DOI:
10.23919/ICIF.2018.8455496
Auteurs:
Ruilong Chen, Md Asif Jalal, Lyudmila Mihaylova, Roger K Moore
Publié dans:
2018 21st International Conference on Information Fusion (FUSION), 2018, Page(s) 565-572, ISBN 978-0-9964527-6-2
Éditeur:
IEEE
DOI:
10.23919/ICIF.2018.8455227
Auteurs:
Hayder M. Amer, Hayder A. A. Al-Kashoash, Andrew Kemp, Lyudmila Mihaylova, Martin Mayfield
Publié dans:
2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2018, Page(s) 306-310, ISBN 978-1-5386-4752-3
Éditeur:
IEEE
DOI:
10.1109/sam.2018.8448582
Auteurs:
Hayder M. Amer, Christos Tsotskas, Matthew Hawes, Patrizia Franco, Lyudmila Mihaylova
Publié dans:
2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2017, Page(s) 1-6, ISBN 978-1-5386-3103-4
Éditeur:
IEEE
DOI:
10.1109/sdf.2017.8126359
Auteurs:
W. Aftab, R. Hostetler, A. de Freitas, M. Arvaneh, L. Mihaylova
Publié dans:
IEEE Transactions on Vehicular Technology, 2018, 2019
Éditeur:
IEEE
Auteurs:
Kim Y., Wang P., Zhu Y., and Mihaylova L.
Publié dans:
In Proceedings of the International Symposium on Sensor Data Fusion: Trends, Solutions and Applications, 2018
Éditeur:
Symposium on Sensor Data Fusion: Trends, Solutions and Applications
Auteurs:
Wang P., Kim Y., Vaci L., Yang H. and Mihaylova L.,
Publié dans:
In Proceedings of the International Symposium on Sensor Data Fusion: Trends, Solutions and Applications, 2018
Éditeur:
Symposium on Sensor Data Fusion: Trends, Solutions and Applications
Auteurs:
L. Mihaylova, W. Aftab
Publié dans:
Proceedings from the NATO SET 262, 2018
Éditeur:
NATO SET 262
Auteurs:
Hawes, M.; Amer, H.M.; Mihaylova, L.S.
Publié dans:
978-0-9964-5274-8, Numéro 1, 2016
Éditeur:
IEEE
Auteurs:
Tamara Djukic, Martijn Breen, David Masip, Josep Perarnau, Joseph Budin, Jordi Casas
Publié dans:
Proceedings of mobil.TUM 2017 conference on Intelligent Transport Systems in Theory and Practice, 2017, Page(s) 1-11
Éditeur:
Elsevier
Auteurs:
R. Chen, M. Hawes, O. Isupova, L. Mihaylova and H. Zhu
Publié dans:
Proceedings of the International Conference on Information Fusion, 2017
Éditeur:
IEEE
Auteurs:
Mazumdar, Suvodeep; Ciravegna, Fabio; Ireson, Neil; Read, Jennifer; Simpson, Emma; Cudd, Peter
Publié dans:
9781614997979, Numéro 1, 2017, Page(s) 102-110
Éditeur:
IOS Press
DOI:
10.3233/978-1-61499-798-6-102
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