This deliverable identifies and characterises barriers and limitations of the transport systems for exploiting big data opportunities. The scope of analysis in the document covers technological issues, as well as social, ethical, legal, political, organisational and institutional aspects of the systems. The characterisation of the barriers takes into account the extent to which they can constitute an absolute limitation for exploiting big data.
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 ...
Big data applications in the transport sector have achieved national and EU-level interest as a driver for future economic growth and at the same time a source of concern, in terms of negative socio-economic impacts. This report reviews current policies implemented in the EU, its Member States and internationally, which support or restrict the (re-) use, linking of and sharing of data, in the context of big data techniques and in the transport sector. Also, the report illustrates in selected examples of transport-related private companies, the types of private sector policies that have been adopted or promoted. While there are not any distinctly big data policies, each political entity has implemented some policies aimed at protecting the privacy of its citizens, encouraging data sharing among private and public sector entities, and develop policies that support the digitalization of the transport sector. Some of the key areas of policy in the transport sector are for instance the implementation of Intelligent Transport System, the increased Open Data policies, Automated Driving, and Smart Mobility. Preceding and in light of these developments, the private sector has also moved ahead to incorporate the use of big data techniques into their own business models as process or product innovations. The potential applications in the transport sector are diverse, as digitalization is a major trend of the transport sector. The report covers six distinct transport sub-sectors, where the application of big data is or potentially could be used. The aim is to highlight the challenges and enablers of data sharing in the different cases. The cases are: Railway Operators, Open Data in the Airport Operator Context, Real-time Road Traffic Management, Big Data in Supply Chain Management, Managing Port operations, and Connected and Automated Vehicles. The development of a policy roadmap to foster the growth of big data in transport will require an understanding of how existing policies affect the economic, political, social and ...
European Union's Transport policy's pivotal aim is to strengthen the existing Transport infrastructure, which is crucial to economic development. The improvement in the transport sector should provide efficient logistics of goods, better travel and commuting facilities, and accessibility of the European region. This report, as part of the first phase of the Leveraging Big Data to Manage Transport Operations (LeMO) project, provides an introduction to big data in the transport sector. It identifies untapped opportunities and challenges and describes numerous data sources. This report is a part of WP1 which is a cornerstone of the LeMO project. It aims to generate a shared understanding of current big data landscape in transport and identifies a holistic view on opportunities, challenges, and limitations. The remainder of this report is structured as follows: Chapter 2 explores the characteristic of big data and highlights the big data challenges in the transport sector. It covers six transportation modes (air, rail, road, urban, water and multi-modal) and two transportation sectors (passenger and freight). Chapter 3 identifies several opportunities and challenges of big data in transportation, by using: several subject matter expert interviews, nineteen applied cases, and a literature review. It also indicates that the combination of different means and approaches will enhance the opportunities for successful big data services in the transport sector. Chapter 4 offers an intensive survey of the various data sources, data producers, and service providers. In addition, cartography was modeled to visualize data flows intuitively. Cartography demonstrates where data originated from and where it is flowing to. Chapter 5 summarizes all findings and provides a conclusion.
