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Big Data for Transportation

Presenters: Laurie A. Schintler, The 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.[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_column_text]

Suitable for:

• Ministries of Transportation
• Motor Vehicle Authorities
• Consultants

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Presenter Bios

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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.

[/vc_column_text][vc_separator color=”grey”][vc_column_text]Dr. Shanjiang Zhu is an Assistant Professor of Transportation Planning and Engineering at George Mason University (GMU). He graduated from Tsinghua University with a B.S degree in 2003 and a M.S in 2005. During 2001-2003, he studied at the Ecole Centrale de Nantes, in France, as a dual-degree student. He obtained his Ph.D. degree at the University of Minnesota, Twin Cities, in 2010 and worked two years as a Research Scientist at the University of Maryland before joining GMU. Dr. Zhu is experienced in travel demand modeling, travel behavior analysis, GPS-based travel survey method, integrated transportation planning and simulation models, traffic incident management, and transportation economics.   He serves on the Transportation Economics Committee (ABE20) and Traveler Behavior and Values Committee (ADB10) of the Transportation Research Board. He is also serving on the Technical Advisory Committee of the Northern Virginia Transportation Authority. Dr. Zhu is PI at GMU of the newly founded TransInfo UTC that focuses on Big Data studies in transportation. He has also been funded by Virginia OTP3 to investigate potentials of multi-modal P3 projects in corridor development. Dr. Zhu is a champion of multi-disciplinary research approach. He holds three Master’s degrees in Civil Engineering, Automatics, and Applied Economics, respectively.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column width=”1/1″][vc_column_text]

Register

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**AASHTO members received complementary access to all IRF webinars** Webinar access information will be provide the day prior to the webinar. Following the event, a recording and PDF of the presentation will be made available to attendees, upon request.[/vc_column_text][/vc_column][/vc_row]