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News
- March 15, 2011: Dr. Jeff Ban received the NSF CAREER award CMMI-1055555 on CAREER: Using Mobile Sensors for Traffic Knowledge Extraction and Dynamic Network Management. This project will take a systematic view on relatively high penetration mobile sensor data, and develop foundations that can best use such data for traffic knowledge learning and dynamic network management. Educational and outreach activities will also be conducted, based on the research, to teach undergraduate and graduate students and to disseminate research results to practitioners, and to outreach to minority universities, academia, government agencies, and the industry. See more information here: http://news.rpi.edu/update.do?artcenterkey=2852 and http://blogger.rpi.edu/approach/2011/04/11/3%C2%B0-with-jeff-ban/
- October 10, 2010: We are recruiting highly motivated graduate students working in the areas
of modeling mobile sensors as traffic probes, transportation network modeling and related applications (such as congestion pricing), large-scale traffic simulation, and ITS in general.
Students with strong backgrounds in traffic engineering, mathematics or operations research, statistics, and optimal control are
encouraged to apply. Please send email directly to Dr. Jeff Ban at banx@rpi.edu.
- September 01, 2010: We received another NSF grant EFRI-1024647 BECS Collaborative Research: Modeling the Dynamics of Traffic User Equilibria Using Differential Variational Inequalities. The project is to investigate, by collaborating with Dr. Jong-Shi Pang at the University of Illinois at Urbana Champaign and Dr. Henry Liu at the University of Minnesota, Twin Cities, the use of differential variational inequaltity (DVI) to model and compute the solutions of dynamic user equilibria (DUE).
- September 01, 2010: We received the NSF grant
CMMI-1031452 Collaborative Research: Mobile Sensors as Traffic Probes - Addressing Transportation Modeling and Privacy Protection in an Integrated Framework. The project is to investigate, by collaborating with Marco Gruteser at the Rutgers University, how to develop privacy protection techniques to collect and process mobile traffic sensors and how to use privacy-preserving mobile sensor data to estimate real time performance measures of signalized intersections (such as delay, queue length, and emissions).
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