The University of Southampton

Transforming Transport

TT consortium- Project kick-off meeting - 12th January 2017, Madrid
Data Science / Big Data, Machine Learning, Environmental Monitoring

Big Data will have a profound economic and societal impact on the mobility and logistics sector, which is one of the most-used industries in the world contributing to approximately 15% of GDP.

Big Data is expected to lead to $500 billion in value worldwide due to time and fuel savings, as well as a significant environmental impact by saving an estimated 380 megatons of CO2 just in the mobility and logistics sector. With freight transport activities projected to increase by 40% in 2030, transforming the current mobility and logistics processes to become significantly more efficient will have a profound impact. A 10% efficiency improvement may lead to cost savings of €100 billion in the EU. Despite these promises, merely 19% of EU mobility and logistics companies employ Big Data solutions as part of value creation and business processes.

The Transforming Transport (TT) project will demonstrate the transformative effects that Big Data will have on the mobility and logistics market. To this end, TT validates the technical and economic viability of Big Data to reshape transport processes and services to significantly increase operational efficiency, deliver improved customer experience, and foster new business models.

TT will address seven pilot domains of major importance for the mobility and logistics sector in Europe: (1) Smart Highways, (2) Sustainable Vehicle Fleets, (3) Proactive Rail Infrastructures, (4) Ports as Intelligent Logistics Hubs, (5) Efficient Air Transport, (6) Multi-modal Urban Mobility, (7) Dynamic Supply Chains.

IT Innovation's work in TT focuses on the Proactive Rail Infrastructures pilot, using our expertise in Big Data analytics, machine learning, data mining and knowledge modelling for investigating the rail network's mechanical and electrical assets in order to provide health assessment and making prognosis of implications for the assets' maintenance regimes. This work builds on past projects, which address research on intelligent data processing and knowledge extraction for critical decision-support such as TRIDEC and ZONeSEC.

Primary investigator

Secondary investigator

  • Zlatko Zlatev


  • Network Rail (UK)

Associated research group

  • IT Innovation Centre
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