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Big Data Analytics in Transportation

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

RandeepSudan
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Big Data Analytics in Transportation

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  1. Big Data in TransportationRandeep SudanAdviser Digital Strategy and Government AnalyticsWorld 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. South Korea’s Plans for Next Generation Transport Source: Mid-to Long-Term Master Plan in Preparation for the Intelligent Information Society Managing the Fourth Industrial Revolution (South Korea)

  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

  17. 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.

  18. 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.

  19. 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

  20. 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.

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

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

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

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

  25. 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. Institutional Structure

  28. Thank You

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