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.
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.
This deliverable reports on communication and dissemination activities of the LeMO project implemented during the first year of the project implementation.
This Deliverable identifies and examines various societal and ethical issues that are relevant to the production of, access to, linking of and re-use of big data in the transport sector.
Chapter 2, on the one hand, discusses the concept of big data, its particular characteristics, and its possible use in the transport sector. On the other hand, it delves into the interaction between ethical and social issues and the ways to integrate these into the existing policy framework. In Chapter 3, the authors examine the various identified ethical and social issues and discuss the challenges and opportunities that may arise in this respect, coming up with notably the following findings:
- Trust: Although the research in trust has already become relatively mature, the huge amount and diversity of data and data sources provides lots of new opportunities but at the same time poses many challenges for online trust, notably in the context of transportation.
- Surveillance: Considering the role of government agencies and their increasing requests of information to the private sector for public security purposes, it appears necessary to adopt specific rules to regulate the information flow, to define the rights over data and to ensure adequate enforcement.
- Privacy (including transparency, consent and control): The advent of the GDPR has had a considerable impact in the domains of privacy, transparency, consent and control. This strengthened legal framework is likely to respond to several ethical issues and thus improve end users' trust in the use of personal data in a big data context.
- Free will: Although big data-driven profiling practices can limit free will, a huge part of what we know about the world comes from data analysis. Careful and appropriate information analysis can open up plenty of chances and might reduce the limitations and problems for free will.
- Personal data ownership: This Deliverable concludes that a claim of ownership by a data subject in its personal data would be hard to sustain. Nevertheless, in a big data context, different third-party entities may try to claim ownership in (parts of) a dataset, which may hinder the use of big data, including in the transport sector.
- Discrimination: Using big data analytics to improve business processes or provide personalised services may lead to discrimination of certain groups of people. Also, the "Digital Divide", i.e. the social differences in access to technology and education or skills to use it, may lead to data-driven discrimination.
- Environmental: There are trade-off or rebound effects from the use of big data in transport, which limits the effect of big data exploitation or creates unintended consequences. Such trade-off or rebound effects will be further assessed in Deliverable D2.4.
Finally, the last Chapter serves as a conclusion and 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. Particularly, Chapter 4 examines whether regulatory intervention or ethics-by-design are appropriate solutions to the challenges caused by ethical and social issues in relation to big data. The conclusion is that regulatory intervention is not desirable. Instead, the authors advocate an approach whereby ethics-by-design is recognised as an EU legal principle, similarly to privacy-by-design, and is supplemented by self-regulation and soft law. Section 4.3.1 aims to provide inspiration for ethics-by-design core implementation principles to be developed further in working groups at EU level.
The proliferation of data availability is reshaping the economic and political realm. On the one hand, big data enables private and public parties to provide better quality services and products. On the other, the usage of data has led to policy response for limiting (e.g. the GDPR) or enabling (e.g. EU’s policy towards a ‘digital single market’) the application of big data.
This report aims at revealing the wider economic and political issues involved with utilizing big data by elaborating on the interaction between transport actors (demand-, supply-, external- and governance actors) and their role in the data economy (as data users, suppliers or facilitators). Subsequently, the interaction between these actors is described on various levels.
On the firm level, private parties use big data use big data for improved situational awareness of the transport system, improving the capacity of transport networks, improving transportation services and facilitating the shift to sustainable transport.
On the industry level, the data economy is expected to grow rapidly in the coming years. While the EU data economy remains to be in a deficit compared to the US regarding structural factors (fewer data SME), cultural/educational factors (ability to create and keep data-related skills), and the presence of IT giants, there is a healthy presence of digital start-ups and innovation capacity.
On the national level, governments utilize big data in improving organisational performance and in service provision and policy making. Subsequently, big data is applied in transport related government tasks, including transport planning, traffic monitoring and public transport provision.
On international level, governments want to control data flows to limit the negative consequences of data, e.g. preventing the misuse of personal or classified data. Another reason is to easier carry out their task as supervisor, e.g. demanding local storage of tax or gambling data to simplify control routines.
Having discussed this, the most critical challenges for the future identified in this report are:
- Lack of data professionals, in particular within governments;
- Government compartmentalization, limiting optimal data usage by governments;
- An insufficient framework that satisfies both the user demand for privacy and the usage of personal information for business innovation. This is particularly relevant for public-private data sharing schemes;
- Too little awareness on the capacity of big data, as ‘bad’ big data analysis happens quickly, i.e. too little knowledge on what transport-related questions it can and cannot address, and how big data should address these questions.
This document is valuable in the objective definition stage of big data applications in transport. It facilitates the discussion on if big data should be used by providing insight into how that may be. Economic actors are provided with an overview of existing applications of big data in the transport sector. Political actors are given insight into how governments are currently using big data.