Dr. Ban’s research interests are in transportation network system modeling and simulation, urban traffic system modeling and operations, and Intelligent Transportation Systems (ITS). He develops modeling tools to study dynamic transportation networks with emerging technologies and systems such as connected/automated vehicles and shared mobility. He also works on urban traffic system state estimation/prediction using mobile sensing data. He is the recipient of the NSF CAREER Award, and the New Faculty Award by the Council of University Transportation Centers and American Road & Transportation Builders Association. His research has been funded by the NSF, US DOT, NCFRP, Volvo Foundation, among others. He joined UW as an Associate Professor in the Fall 2016. Prior to this appointment, he was an Associate Professor of the Department of Civil and Environmental Engineering at the Rensselaer Polytechnic Institute.
Dr. Ban serves on the Network Modeling Committee (ADB30) and the Vehicle Highway Automation Committee (AHB30) of Transportation Research Board (TRB) of the National Academies. He was the elected Vice Chair (2010-2011) and Chair (2012-2013) of the ITS SIG (cluster) under Transportation Science and Logistics (Society) of INFORMS. He currently directs the intelligent Urban Transportation Systems (iUTS) lab at UW. He is an Associate Editor of IEEE Transactions on Intelligent Transportation Systems, Journal of Intelligent Transportation Systems, Networks and Spatial Economic, and Transportmetrica B: Dynamics, and serves on the editorial board of Transportation Research Part B, Part C. His research has produced more than 100 papers in refereed journals and conference proceedings.
- Ph.D. in Civil and Environmental Engineering, University of Wisconsin, Madison, 2005
- M.S. in Computer Sciences, University of Wisconsin, Madison, 2003
- M.S. in Automotive Engineering, Tsinghua University, P. R. China, 2000
- B.S. in Automotive Engineering, Tsinghua University, P. R. China, 1997
- Associate Professor, Civil and Environmental Engineering, Rensselaer Polytechnic Institute
Dr. Ban’s research goal is to help resolve transportation related congestion, energy and emission problems, by promoting new technologies and transportation options in a way that is beneficial to the society at large. For this, he uses mathematical modeling and computer simulation techniques to explore how various transportation systems operate in tandem, from freight trucks to passenger cars to transit vehicles, and how they may be managed and/or operate cooperatively for the common good. To develop more efficient, safer transportation systems, he utilizes insight from vehicle sensors, as well as other data gathering methods, to learn/predict vehicle and transportation system performances. Recently, he develops modeling tools to study dynamic transportation networks with emerging technologies and systems such as connected/automated vehicles and shared mobility. He also works on urban traffic system state estimation and prediction using mobile sensing data.
Pacific Northwest Transportation Consortium (PacTrans), USDOT, December 2016 – November, 2021.
Connected Cities for Smart Mobility Center (Tier 1), USDOT, December 2016, November 2021.
CAREER: Using Mobile Sensors for Traffic Knowledge Extraction and Dynamic Network Management, National Science Foundation (NSF), May. 01, 2011 – Apr. 30, 2017.
Collaborative Research: Transportation network identification - Information Fusion via Stochastic Optimization, NSF, July 01, 2015 – June 30, 2018.
Honors & awards
- School of Engineering Research Excellence Award (Junior Faculty), Rensselaer Polytechnic Institute, 2014
- New Faculty Award, Council of Transportation Research Centers (CUTC) and American Road & Transportation Builders Association (ARTBA), 2012
- CAREER Award, National Science Foundation, 2011
- UTRC’s 2008 Best Paper Award, University of Transportation Research Center Region 2, 2008
Pandemic work and leisure habits studied
A new regional survey uncovers commute, work and lifestyle changes during the pandemic.
A mobility informed COVID-19 transmission model
A new type of mobility informed infectious disease model is already being used by the Centers for Disease Control and Prevention (CDC) to forecast COVID-19 deaths across the country.