Understanding and Mapping Big Data in Transport Sector

BY Kim Hee (1), Naveed Mushtaq (1), Hevin Özmen (1), Marten Rosselli (1), Roberto V. Zicari (1), Minsung Hong (2), Rajendra Akerkar (2), Sophie Roizard (3), Rémy Russotto (3), Tharsis Teoh (4)

Goethe‐University Frankfurt 1, Western Norway Research Institute 2, Confederation of Organizations in Road Transport 3, Panteia B.V. 4

April 2018

“The literature review revealed that the major barriers to big data application in transportation are data silos, data ownership issue, data privacy, and the lack of data quality and standards. One of the most important challenges, however, a lack of expertise of technical knowledge.”

1 Introduction

1.1 Abstract
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 visualizes numerous data sources. The authors believe that this report generates a shared understanding of harnessing big data and provides a foundation for phase 2 and 3 of the LeMO project.

This report is a part of work package 1 (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. Chapter 3 identifies the opportunities and challenges in research, applied cases from governmental, non‐governmental and private organizations. Chapter 4 provides an extensive survey of big data sources with the cartography of data flows. Chapter 5 summarizes all findings and provides conclusion. Finally, qualitative interviews with subject matter experts are provided in three Appendices.

1.2 Purpose of the document

Functionality and efficiency of the transport sector significantly rely on data such as sensor‐ generated data, traffic schedules, flight information and more. Due to the recent developments in Information and Communication Technology (ICT), a large amount of data is generated, collected and processed in the transport sector. This provides untapped opportunities to gain better insight into transportation infrastructure, movement of people and vehicles. However, numerous challenges such as data silos, poor data quality and lack of expertise hinder to seize such opportunities. Thus, the demand to understand the transport sector associated with big data is surging rapidly from the transport stakeholders.
To meet the current needs, the LeMO project aims to provide a comprehensive view that is amplifying opportunities, while diminishing limitations. The LeMO project is comprised of three phases.
Phase 1 investigates the role of big data in the transport sector and identifies institutional and governmental issues.
Phase 2 explores the societal impact of comprehensive case studies based on the findings of Phase 1.
The findings of Phase 1 and 2 will feed into exploring the future direction in Phase 3. The created value from the course of all three phases will be disseminated through various channels in parallel.

1.3 Target audience
The target audience of this report comprises of government policy‐makers, transport industry, and relevant technology companies. For example, part of this report attempts to help one to make an evidence‐driven decision. And the course of the decision may introduce a new policy in the transport sector, which could impact a change in people’s lifestyle. Nevertheless, the target audience can be anyone who is interested in examples of big data technologies in general, since it offers a comprehensive overview of harnessing big data.

LINK to DOWNLOAD .PDF (70 pages)

Horizon 2020 Research and Innovation Programme
MG‐8‐2‐2017 ‐ Big data in Transport: Research opportunities, challenges and limitations

LeMo- LEVERAGING BIG DATA FOR MANAGING TRANSPORT OPERATIONS
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 770038.

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