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Big Data for Transportation

May 28, 2014 @ 11:00 - 12:30 EDT

Big Data for Transportation

Presenters: Laurie A. SchintlerThe School of Public Policy, George Mason University Shanjiang Zhu, Volgenau School of Engineering, CIEE, George Mason University   Big Data in transportation has taken off, although the exact definition of this concept is still widely debated among researchers, practitioners, and government officials. Big Data usually refers to enormous volumes of information of diverse structures (or lack of structure) that cannot be analyzed using conventional relational databases and data mining techniques. Big Data for transportation can be generated by sources such as smartphones, digital photography, video monitoring systems, GPS, real-time control systems in automobiles, automatic vehicles, social media, etc.   Successful exploration of Big Data creates values and present unlimited opportunities in many ways: providing real-time predictions with substantially higher accuracy, supporting strategic and/or proactive decision-making, allowing precisely tailored services, and many others. Because of this potential, Big Data have attracted increasing interests. USDOT has organized a series of conferences covering this topic. The Journal of Transportation Research dedicated a special issue on this topic to synthesize the latest progresses. Recently, George Mason University, as part of a consortium, was awarded by USDoT to establish Transportation Informatics University Transportation Center (TransInfo UTC) that focuses on harnessing the power of Big Data in support of USDOT strategic goals. This presentation intends to give a quick survey on the latest development in Big Data research and the usefulness of Big Data for transportation planning and policy purposes.   Specific topics we intend to cover include:   1. What is Big Data?   2. What is the value and potential of using Big Data for transportation purposes (operations, planning and policy development)? 3. What are the technical and computational challenges associated with the use of Big Data?   To help answer these questions, we will draw upon a number of cases studies and applications – e.g., those related to connected vehicles, vehicle-to-infrastructure communication, and location-based services. We will also touch on opportunities for the developing world.

Suitable for:

  • Ministries of Transportation
  • Motor Vehicle Authorities
  • Consultants

Presenter Bios

Laurie Schintler Laurie Schintler is Associate Professor at the School of Public Policy at George Mason University, where she teaches graduate courses on transportation theory and models, regional development theory, and statistics and econometrics. Professor Schintler has written numerous articles and papers in her field, including “A Prototype Dynamic Transportation Network Model” and “Evaluation of the Smart Flexible Integrated Real-time Enhancement System (SaFIRES)”.

Professor Schintler is Book Review Editor for the Annals of Regional Science, and, among other service activities, is helping the Metropolitan Washington Council of Governments design and set up a web site for complaints regarding signalized intersections in the Washington region. Professor Schintler received her Ph.D. in Regional Planning from the University of Illinois at Urbana-Champaign.

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