Novel decision support tool for evaluating strategic Big Data investments in transport and intelligent mobility services

NOESIS addresses the urgent need to understand and predict the impact of Big Data applications and technologies in transport and logistics. It achieves this by building a database of Big Data in Transport use cases and by developing a Decision Support Tool providing the anticipated benefits and costs for society, users and stakeholders, evidenced by the collected Big Data use cases.

The project will use Big Data techniques to understand and assess the value of Big Data investments in transport.

NOESIS Methodology is based on three pillars:

NOESIS Methodology is based on three pillars:

Pillar 1: Transport Features and Use Cases

  • Linking Big Data products to Transport challenges and use cases
  • Identification of relevant features for generating value from big data investments
  • Set-up of the NOESIS Big Data in Transport Library

Pillar 2: Learning Framework/Process

  • Architecture Development of the Decision Support Tool for policy makers
  • Classifying Big Data applications in transport, based on machine learning techniques and identifying their benefits and limitations
  • Use of NOESIS Big Data Library and KPIs to validate the Decision Support Tool

Pillar 3: Value capture mechanism

  • Definition of a set of evaluation criteria
  • Impact assessment methodology (IAM)
  • Translating the benefits of IAM into the involved parties

The NOESIS partners include Ortelio Ltd (UK) coordinatockholm (SE), Macomi (NL), FTTE- University of Belgrade Faculty of Transport and Traffic Engineering (RS), BADW-LRZ- Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (DE).

NOESIS project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769980.

To access advice and support please contact CUE Business Solutions:

Tel:     02476 236 406
or fill out our contact form



T | 02476 236 406
E |

Registered Company: 2409655

Social Networks