Dr. Vassilis Papayannoulis has 28 years of experience, and before joining IBI Group he was a Principal and the Travel Demand Forecasting Regional Manager for the New York Office of Cambridge Systematics (CS). From 1990 to 2008, he worked at Urbitran Associates, Inc. (UAI), where as a Senior Vice President he directed the Transportation Modeling group. Dr. Papayannoulis's expertise includes travel demand forecasting, traffic operation simulation, database management systems, safety, system performance measures, software development and Geographic Information Systems (GIS). He has also taught transportation courses at Polytechnic University and City College, in New York City.
Dr. Papayannoulis has been involved in a number of travel demand models including the New York Metropolitan Transportation Council Best Practice Model, which was one of the first activity based models in the nation. Because of his technical knowledge and indepth understanding of the issues related to traffic simulation, he has also been engaged in the development of numerous large scale and complex models, including models addressing special events. Currently his technical focus has shifted to mesoscopic models, Dynamic Traffic Assignment (DTA) and real-time data. He was the technical lead for the Minneapolis and Dallas Integrated Corridor Management (ICM) Pioneer sites, where DTA models were utilized to assess ITS technologies for Incident Management and guidelines for Performance Measures utilizing outputs from mesoscopic traffic simulation models were developed. Also he was the Project Manager for NYCDOT’s Manhattan Traffic Model, where a platform supporting corridor management planning, design and operations based on a multi-resolution model (interfacing travel demand, DTA (mesoscopic) and microscopic simulation models) was developed. Most recently Dr. Papayannoulis participated in the Peer Review Panel for San Francisco County Transportation Authority (SFCTA) DTA project.
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DARPA (Defense Advanced Research Projects Agency) seeks methods to accurately forecast the spread of
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