About Mingjian Wu

Beginning in January 2020, Mingjian started his Ph.D career under the supervision of Dr. Tae J. Kwon after successfully defending his M.Sc thesis in December 2019. His M.Sc thesis research was on quantifying the safety effects of driver feedback sign (DFS) and its location allocation strategies under the co-supervision of Dr. Kwon and Dr. Karim El-Basyouny. He completed his undergraduate studies in Hebei University of Engineering in China where he obtained his BEng in 2017 before coming to the University of Alberta's Transportation Engineering Department. His Ph.D research mainly centers around smart and sustainable transportation with the focus on winter transportation systems, using a lot of techniques such as AI, computer vision, large-scale optimization and geostatistics.  

Doctor of Philosophy

• Date of Defence: November 28, 2024

• Thesis topic: Advancing Winter Road Surface Condition Monitoring through Deep Learning and Geostatistics

Master of Science Degree

• Date of Defence: December 16, 2019

• Thesis topic: Evaluating the Safety Effects of Driver Feedback Signs and Citywide Implementation Strategies 

Education

• M.Sc (2019) in Transportation Engineering from the University of Alberta

• B.Eng (2017) in Civil Engineering at Hebei University of Engineering

Research Interests

• Sustainable transportation, including winter transportation, road weather information systems, etc.

• Deployment optimization of Intelligent Transportation System (ITS) facilities

• Geographical Information Systems (GIS) and Remote Sensing technologies of transportation engineering

• Applications of artificial intelligence (AI) in modelling road surface and traffic conditions

• Computer vision techniques in automating road surface monitoring

• Geostatistical modelling of winter road surface conditions and performance measures

Academic Events

• Towards Strategic and Equitable RWIS Network Planning and Management. Invited seminar at the IEEE ITSC 2024 workshop session on Equity, Accessibility, and Inclusiveness in ITS.

• Continuous Mapping of Winter Road Surface Conditions via Deep Learning and Geostatistics. Invited presentation at the AKR50 committee meeting at the 101st Transportation Research Board Annual Conference.

• Optimization Models for Snowplow Routes and Depot Locations: A Real-world Implementation. Invited lectern session at the 101st Transportation Research Board Annual Conference.

• A Citywide Location-Allocation Framework for Driver Feedback Signs: Optimizing Safety and Coverage of Vulnerable Road Users. Invited lectern session at the 100th Transportation Research Board Annual Conference.

• Spatial Mapping of Winter Road Surface Conditions Using Geostatistics. Invited online presentation at the AKR40 committee meeting at the 100th Transportation Research Board Annual Conference.

• Advancing Winter Transportation Safety, Mobility, and Sustainability using Geostatistics and Deep Learning. Invited online guest lecture (with Dr. Tae J. Kwon) at Shandong University.

• A Safety Assessment of Driver Feedback Signs and Development of Future Expansion Program. Invited online presentation at monthly webinar of Canadian Institute of Transportation Engineers (Northern Alberta Section). 

Publications

• Wu, M., & Kwon, T. J. (2024). Weather event characterization: a catalyst for improved spatial mapping and benefit quantification in winter road maintenance. Cold Regions Science and Technology, 104208.

• (*Editor’s Choice) Wu, M., & Kwon, T. J. (2023). Location–allocation strategies for traffic counters—a citywide deployment. Canadian Journal of Civil Engineering.

• Wu, M., Kwon, T. J., & Huynh, N. (2022). Winter Road Surface Condition Recognition Using Semantic Segmentation and the Generative Adversarial Network: A Case Study of Iowa, USA. Transportation Research Record, 03611981231188370.

• Wu, M., Kwon, T. J., & Fu, L. (2022). Spatial mapping of winter road surface conditions via hybrid geostatistical techniques. Journal of Cold Regions Engineering, 36(4), 04022009.

• Wu, M., & Kwon, T. J. (2022). An Automatic Architecture Designing Approach of Convolutional Neural Networks for Road Surface Conditions Image Recognition: Tradeoff between Accuracy and Efficiency. Journal of Sensors, 2022.

• Wu, M., & El-Basyouny, T. J. K. K. (2022). A Hybrid Geostatistical Method for Estimating Citywide Traffic Volumes–A Case Study of Edmonton, Canada. Journal of Geographical Research| Volume, 5(02).

• Wu, M., Kwon, T. J., Fu, L., & Wong, A. H. (2022). Advances in sustainable winter road maintenance and management for future smart cities. In The Rise of Smart Cities (pp. 625-659). Butterworth-Heinemann.

• Xu, S., Wu, M., Kwon, T. J., & Perchanok, M. (2022). Optimization Models for Snowplow Routes and Depot Locations: A Real-World Implementation. Transportation Research Record, 03611981221077264.

• Wu, M., Kwon, T. J., & El-Basyouny, K. (2020). A Citywide Location-Allocation Framework for Driver Feedback Signs: Optimizing Safety and Coverage of Vulnerable Road Users. Sustainability, 12(24), 10415.

• Wu, M., El-Basyouny, K., & Kwon, T. J. (2020). Before-and-after empirical Bayes evaluation of citywide installation of driver feedback signs. Transportation research record, 2674(4), 419-427.

• Wu, M., El-Basyouny, K., & Kwon, T. J. (2021). Lessons learned from the large-scale application of Driver Feedback Signs in an urban city. Journal of Transportation Safety & Security, 13(12), 1283-1301. 

• Gu, L., Wu, M., & Kwon, T. J. (2020). An enhanced spatial statistical method for continuous monitoring of winter road surface conditions. Canadian Journal of Civil Engineering, 47(10), 1154-1165.

• Biswas, S., Wu, M., Melles, S. J., & Kwon, T. J. (2019). Use of topography, weather zones, and semivariogram parameters to optimize road weather information system station density across large spatial scales. Transportation research record, 2673(12), 301-311.