The presentation touches upon the use of Big Data Analytics in Transportation

1. Big Data in Transportation
Randeep Sudan
Adviser Digital Strategy and Government Analytics
World Bank

2. The Future of Logistics
Big Data, Cloud, IoT, AI and Autonomous Vehicles

3. Source: DHL Trend Radar

4. The Power of Analytics
Some examples

5. Big Data in Transportation
• Data analytics can provide insights for every aspect of transportation
• Planning of transportation infrastructure
• Traffic management
• Customized, flexible and convenient public transportation systems
• Influencing behaviour
• Fleet maintenance

6. Australia: Infrastructure Planning

7. Congestion management
• In Europe infrastructure congestion
costs 1% of GDP (McKinsey)
• Mobile service providers and navigation
companies collect billions of traffic
measurement points daily; using this
data to reduce congestion could result,
by 2020, in worldwide savings of
US$500 billion in time and fuel, and 380
megatons of CO2 emissions.

8. Singapore
• Implementing a system based on Global
Navigation Satellite System (GNSS) technology
and in-vehicle units to get aggregated,
comprehensive and real-time data on road traffic.
• Better switching of traffic lights
• Distance, time, location and vehicle type based
road pricing scheme

9. Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth
Industrial Revolution (South Korea)
South Korea’s Plans for Next Generation Transport

10. Data analytics as behavioral tool - Uber
• “We show drivers areas of high demand or
incentivize them to drive more,” said Michael
Amodeo, an Uber spokesman.
• Drivers sent their next fare opportunity before
their current ride is over.
• In the future monitoring braking and
acceleration speed, could indicate whether
someone is driving erratically and may need to
rest.

11. GE Transportation’s Evolution Series Tier 4 Locomotive
• Has more than 200 sensors that collect
gigabytes of information, processing over
one billion instructions per second.
• The Tier 4 uses on-board edge
computing to analyze data and apply
algorithms for running smarter and more
efficiently.

12. Government’s Role
Some examples

13. “As Chief Data Scientist, DJ will help shape policies and practices to help the U.S.
remain a leader in technology and innovation, foster partnerships to help
responsibly maximize the nation’s return on its investment in data, and help to
recruit and retain the best minds in data science to join us in serving the public”.

14. Duties: Chief Data Officer (US Department of Transportation)
• Manage the open government data effort, including coordinating how we offer
APIs and create public data products.
• Increase the effectiveness in efficiency in managing data, analyzing the public
value of the data we have, and collaborating across the Department to share
data.
• Improve how the agency collects, uses, manages, and publishes data.
• Lead the agency efforts to track data collections, data purchases, databases,
physical data models, and linkages between datasets.
• Improve data quality and how we measure data quality
Source: USA Jobs

15. Data Analytics Institutional Capabilities: Singapore

16. New South Wales Data Analytics Center
• Establish and maintain a register of data assets
• Coordinate consistent data management definitions
and standards
• Advise on making de-identified data open to the
public
• Advise on best practice data analytics, cyber
security and privacy measures

18. Public Private Partnerships
• U.S Department of Transport’s partnership with
Sidewalk Labs, a subsidiary of Google’s parent
company Alphabet, to develop Flow.
• Flow is a data and analytics platform designed to
create a management and monitoring system for
public transportation, using aggregated, anonymized
data from billions of trip miles, including proprietary
data from Waze and Google Maps.

19. Communication from the European Commission in the context
of Building a European Data Economy
• In transport, the shift towards cooperative, connected and automated mobility
can reduce accidents, pollution and congestion, and enhances traffic and capacity
management as well as energy efficiency.
• In this context, standards ensuring interoperability across transport
infrastructure, data, applications, services and networks are key.
• Switzerland and Norway have expressed their readiness to cooperate on cross-
border trials on road safety, access to data, data quality and liability, connectivity
and digital technologies.

20. EU’s General Data Protection Regulation (GDPR)
• Will strengthen and unify data protection for all individuals within the EU
• The GDPR will give citizens and residents control of their personal data and will
simplify the regulatory environment for international business by unifying the
regulation within the EU
• The regulation was adopted on 27 April 2016 and will apply from 25 May 2018

21. Germany: White Paper on Digital Platforms
• Creating a legal framework to prevent exclusivity rights to data which hamper
competition.
• Access to data is to be strengthened by using cartel law and also by way of sector-
specific regulations.
• European General Data Protection Regulation creates a good foundation for more
data sovereignty and portability as well as providing the right incentives for the
anonymization and pseudonymization of data.
• Companies must also provide information about the commercial use of personal
data so that users are made more aware that seemingly free services are funded
by the sale of data.

22. Big Data Strategy: Data continuum
• Generation
• Capture
• Transmission
• Storage
• Security
• Sharing
• Analytics
• Cognification
• Legal, regulatory and institutional

23. South Korea Showcase
Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society
Managing the Fourth Industrial Revolution

25. Data goals
Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth
Industrial Revolution (South Korea)

26. Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth
Industrial Revolution (South Korea)

27. Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth
Industrial Revolution (South Korea)

28. Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth
Industrial Revolution (South Korea)

29. Institutional Structure

30. Thank You