Kun Xie, PhD
Assistant Professor
Transportation Informatics Lab
Research Areas
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Transportation Safety
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Statistical and Econometric Modeling
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Big Data Analytics
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Machine Learning in Transportation
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Transportation Reliability and Resilience
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Incident/Emergency Management
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Connected and Autonomous Vehicles
About Me
I am an Assistant Professor in the Department of Civil and Environmental Engineering at Old Dominion University (ODU) and the director of the Transportation Informatics Lab. Prior to joining ODU, I worked as a Lecturer (equivalent to Assistant Professor in USA) in the Department of Civil and Natural Resources Engineering at University of Canterbury and as a Postdoctoral Associate in the Center for Urban Science and Progress (CUSP) and Department of Urban and Civil Engineering at New York University. My research concentrates on the development of data-driven solutions and emerging technologies to enhance the safety, efficiency and resiliency of transportation systems. I have published over 100 refereed papers in scholarly journals and conference proceedings. I am recognized by prestigious research awards such as IEEE ITS Best Dissertation Award, CUTC Milton Pikarsky Memorial Award and Transportation Research Board (TRB) Best Paper Award. I have successfully helped secure external funding from National Science Foundation (NSF), US Department of Transportation (USDOT) and American International Group (AIG).
EXPERIENCE
Jul 2019-Present
Assistant Professor
Aug 2017-Jul 2019
Lecturer (Assistant Professor)
@ Christchurch, New Zealand
Department of Civil and Natural Resources Engineering
Jan 2017-Aug 2007
Postdoctoral Researcher
@ New York City, USA
Center for Urban Science and Progress (CUSP) and Department of Urban and Civil Engineering
EDUCATION
Dec 2016
Doctorate Degree
@ New York City, USA
Ph.D. in Transportation Planning and Engineering
Dissertation: New Opportunities in Urban Safety Analytics Using Advanced Quantitative Methods and Big Data (IEEE ITSS Best Dissertation Award)
Aug 2012
Master's Degree
@ Shanghai, China
Master of Transportation Planning and Management
Thesis: Research on Signalized Intersection Safety Using Spatial Analysis and Bayesian Methods
Aug 2009
Bachelor's Degree
PARTNERS
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RESEARCH
TEACHING
CEE 370. Transportation Fundamentals
This course introduces undergraduate civil engineering students to the basic principles of highways and traffic engineering. It covers several topics including transportation planning, highway geometric design, traffic operations, traffic control, and traffic safety. This introductory course is required for the successful completion of the civil engineering degree at ODU. It is also a prerequisite for other elective transportation courses offered in the curriculum.
CEE 770/870. Transportation Safety
This course is designed to teach graduate students the fundamentals of transportation safety and the use of data-driven approaches for safety applications. It includes topics such as crash data analysis, modeling crash observations (e.g., frequency, severity, and duration), the procedure of roadway safety management, hotspot identification, before-after evaluation of safety treatments, and surrogate safety measures. It also covers advanced topics such as the use of advanced statistical models, artificial intelligence, big data, and emerging technologies to address safety issues.
CEE 777/877. Econometric Modeling in Transportation
This course focuses on the development of econometric modeling and its application in the field of transportation engineering. Topics to be covered include statistical inference, linear regression, count data models, discrete choice models, survival analysis, time series modeling, spatial modeling, panel data analysis, and structural equation modeling. Students will have a better understanding of the concepts and theories of econometrics and will be equipped with well-suited modeling and analysis techniques.