Deliverable 3.2 Case study reports on constructive findings on the prerequisites of successful big data implementation in the transport sector

The deliverable presents seven reports of the case studies conducted in Work Package 3 during Task 3.2. The case studies conducted are the following:

• Case study 1 “Railway transport”

• Case study 2 “Open data and the transport sector”

• Case study 3 “Real-time traffic management”

• Case study 4 “Logistics and consumer preferences”

• Case study 5 “Smart inland shipping”

• Case study 6 “Optimised transport & improved customer service”

• Case study 7 “Big data and intelligent transport systems”

The methodology outlined in D3.1 “Case study methodology” was used as a template for each of the case studies. The template provided a consistent, but flexible approach to address the unique circumstances and learnings in each case study. It also leveraged the case study leaders’ strengths in understanding the applications of big data technology in transport operations.

Besides developing a deep understanding of the big data technology and its business applications, the case studies also present an analysis of the issues that serve as ‘opportunities’ and ‘barriers’ to the implementation of big data, as well as the resulting outcomes of the implementation. These issues were analysed using the knowledge developed in Work Packages 1 and 2 of the LeMO project, from economic, political, social and ethical, legal, and environmental perspectives.

Deliverable 2.4 on Trade-Off from the Use of Big Data in Transport

In this report different types of rebound effects are addressed. This encompasses direct and indirect rebound effects, as well as society-wide, or overall rebound effects. Further different application areas of rebound effects are presented. They cover rebound effects in connection with energy efficiency measures and climate gas reduction measures, as well as in connection with measures aimed to reduce other environmental pollutants. Critique of the rebound effects is also presented. We then turn to strategies to mitigate the rebound effect and suggest approaches for assessment of rebound effects from the use of big data in transport. Two very different approaches are presented, that either focus on 1) the ICT-infrastructure or 2) the transport system. The two approaches are addressed in the two following chapters, first in the analysis of rebound effects from ICT and cloud computing (Chapter 5), then in connection with the LeMO case studies (Chapter 6). Passenger transport and freight transport are dealt with separately, also in connection with the various transport modes: road, rail, water and urban transport. The purpose of the chapter 7 is to map the aspects that may be relevant for further research in LeMO case studies and may be further elaborated in the next project phase in a report with consolidated case study findings.

Deliverable D2.2 Report on Legal Issues

This Deliverable identifies and examines various legal issues that are relevant to the production of, access to, linking of and re-use of big data in the transport sector.

Chapter 2 sets the scene and introduces the concept of big data, its particular characteristics, its possible use in the transport sector, the existing policy framework, and the identified legal issues.

In Chapter 3, the authors examine the various identified legal issues and discuss the challenges and opportunities that may arise in this respect.

Finally, this deliverable introduces possible ways of moving forward to encourage the production of, access to, linking of and re-use of big data in the transport sector, with a particular focus on the EU. The several improvements suggested by this Deliverable vary between the different legal issues and range from avoiding restrictive interpretations by the relevant authorities or courts, over soft law measures (such as guidelines and codes of conduct), to regulatory intervention at EU level.