NOvel Decision Support tool for Evaluating Strategic Big Data investments in Transport and Intelligent Mobility Services

Projektinformationen

NOESIS

ID Finanzhilfevereinbarung: 769980

DOI

10.3030/769980

Projekt abgeschlossen

EK-Unterschriftsdatum 7 August 2017

Startdatum 1 November 2017

Enddatum 31 Oktober 2019

Finanziert unter

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Gesamtkosten

€ 1 197 831,25

EU-Beitrag

€ 1 197 831,25

Koordiniert durch

ORTELIO LTD
United Kingdom

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Documents, reports (14)

Data governance and institutional issues

A report on big data governance and institutional issues.

Big Data and emerging transportation challenges

A report describing the research challenges related to big data technologies and methods for the transport sector.

Exploitation Plan

This deliverable will report the activities for exploiting the research results of the project after the project’s life time.

Big Data implementation context in transport

A report describing the review and analysis of the Big Data investments and services

Data Benefit Analysis and Impact Assessment Methodologies (IAM) for appraising big data solution in transport

This deliverable will consist of the Data Benefit Analysis and the Impact Assessment Methodology for the evaluation of investments in big data to improve management and optimization of transport systems and networks.

Policy briefs

Four targeted (4) policy briefs by the end of the project

Big Data in Transport Library

A report with all the use cases identified in NOESIS

Dissemination , communication and exploitation plan

A report on planned dissemination, communication and exploitation activities.

Suitability of business and organizational models for the successful implementation of big data in transport solutions

This deliverable will provide a set of recommendations for the right implementation of successful business models for using of big data for transport. The analysis will make recommendations depending on the freight vs. passenger transport and the specific characteristic of each transport mode, or the interconnection of different transport modes.

Handbook on Key Lessons Learnt and Transferable Practices

A report on the key lessons learns and best practices derived from the use cases analysis.

Learning Framework methodology and architecture

A report describing the Learning Framework methodology and architecture of NOESIS.

Technological and policy roadmaps

This deliverable will produce two coordinated roadmaps: one for the implementation of technological solutions and the other one for policy measures aimed at facilitating the use of big data to public agencies and transport companies.

Development and validation of the NOESIS Decision Support tool (DST)

A report describing the development and validation of the NOESIS Decision Support tool (DST)

Summary to Practitioners on Laws, Regulations, and Directives on Data Privacy, Security and Openness

A Report summarizing all Big Data related Laws, Regulations, and Directives related to transport.

Veröffentlichungen

Conference proceedings (4)

MONGODB DATABASES IN BIG DATA APPLICATIONS IN TRANSPORTATION INDUSTRY

Autoren: Janković, S., S. Mladenović, S. Zdravković, S. Vesković, and A. Uzelac,
Veröffentlicht in: "Second International Conference ""Transport for Today's Society""", 2019, ISBN 9789-989786778
Herausgeber: Faculty of Technical Sciences Bitola
DOI: 10.20544/tts2018.p02

SMART TRANSPORTATION PLATFORM FOR BIG DATA ANALYTICS AND INTERCONNECTIVITY

Autoren: Nandor Verba, Kuo-Ming Chao, Soizic Linford, Eleni Anoyrkati
Veröffentlicht in: Proceedings of the Fourth International Conference on Traffic and Transport Engineering, Ausgabe Belgrade 2018, 2018, Seite(n) 232-238, ISBN 978-86-916153-4-5
Herausgeber: City Net Scientific Research Center Ltd. Belgrade

Time series classification using imbalanced learning for real-time safety assessment

Autoren: Katrakazas C., Antoniou C., Yannis G.
Veröffentlicht in: Transportation Research Board (TRB), Ausgabe Proceedings of the Transportation Research Board (TRB) 98th Annual Meeting,, 2019
Herausgeber: TRB

Data Analysis on Big Data Applications with Small Samples and Incomplete Information

Autoren: Soizic Linford, Benjamin Bogdanovic, Kuo-Ming Chao, Sladana Jankovic, Vladislav Maras, Mirjana Bugarinovic, Ilias Trochidis
Veröffentlicht in: 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2019, Seite(n) 146-151, ISBN 978-1-7281-0350-1
Herausgeber: IEEE
DOI: 10.1109/cscwd.2019.8791927

Peer reviewed articles (3)

Schema on read modeling approach as a basis of big data analytics integration in EIS

Autoren: Slađana Janković, Snežana Mladenović, Dušan Mladenović, Slavko Vesković, Draženko Glavić
Veröffentlicht in: Enterprise Information Systems, Ausgabe 12/8-9, 2018, Seite(n) 1180-1201, ISSN 1751-7575
Herausgeber: Taylor & Francis
DOI: 10.1080/17517575.2018.1462404

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Autoren: Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo-Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, Zahid Usman
Veröffentlicht in: Future Generation Computer Systems, Ausgabe 90, 2019, Seite(n) 435-450, ISSN 0167-739X
Herausgeber: Elsevier BV
DOI: 10.1016/j.future.2018.06.042

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Autoren: Szu-Yin Lin, Chi-Chun Chiang, Jung-Bin Li, Zih-Siang Hung, Kuo-Ming Chao
Veröffentlicht in: Future Generation Computer Systems, Ausgabe 89, 2018, Seite(n) 446-454, ISSN 0167-739X
Herausgeber: Elsevier BV
DOI: 10.1016/j.future.2018.06.052