Upcoming Events:
Publication
Wei, T., Batley, R., Liu, R., Xu, G., & Tang, Y. (2024). A method of time-varying demand distribution estimation for high-speed railway networks with user equilibrium model. Transportation Research Part E: Logistics and Transportation Review, 189, 103679.
Presentations:
Tang, Y. (Sept 10, 2025). Exploration of railway predictive maintenance strtegies and AI monitoring. Railway Research Advisory Board (RRAB), Montréal, QC
Buchunde, Suryakant; Tang, Yili (April 1-4 2025). Investigating Departure Time Flexibility in Urban Rail: The Impact of Travel Time, Departure Hours, and Trip Type .11th International Conference on Railway Operations Modelling and Analysis, RailDresden
Wei, Tangjian; Tang, Yili (Kelly); Wang, Zhongshuai (Oliver); Chang, Ben; Juan, Hiedra Cobo (April 1-4, 2025). A Spatial-Based Method of Railway Track Gauge Measurement Based on Lidar Data - 11th International Conference on Railway Operations Modelling and Analysis, RailDresden
Zhang, Zelin; Tang, Yili (April 1-4, 2025). Railway Track Gauge Measurement Based on LiDAR and Camera. 11th International Conference on Railway Operations Modelling and Analysis, RailDresden
Buchunde, Suryakant; Tang, Yili (Jan 6, 2025). How Much Can Passengers Deviate from Their Commuting Schedule? A Flexibility Analysis of Passengers’ Departure Time. Convention Center, Hall A. TRB Annual Meeting
Tang, Y. (Dec 5, 2024). Railway Research Advisory Board (RRAB). Transport Canada. Ottawa (ON)
Tangilan Wel, Yili (Kelly) Tang, Xinyu Lu, Ollver Wang, Juan Hiedra Cobo; Mohamed Zaki Hussein (April 10, 2024). Automated Sensing Data Analysis for Railway Track Gauge Monitoring and Defect Detection. Canadian & Cold Regions Rail Research Conference
We tackle the challenge of managing and analyzing the large volumes of data generated by infrastructure systems using advanced tools such as SmartCard, GPR, LiDAR, high-speed cameras, and environmental sensors. The goal is to enhance data collection, storage, and integration techniques from diverse sources, ensuring more efficient demand management, infrastructure investments and supporting timely, informed decision-making.
Tools for estimating maintenance strategies and conditions of railway infrastructure.
Feedback, collaborations and validations are welcome. Please contact Dr. Yili Tang, ytang564@uwo.ca
Research Objectives
To develop multidimensional anomaly detection and pattern recognition algorithms for visble and invisible defects and early warning system.
To establish lightweight sensing algorithms such as computer vision algorithms to interpret image and video data, LiDar data for 3D reconstructing, IMU for dynamic motions, etc.
To develop short-term and long-term predictive maintenance models for dynamic maintenance schedules and risk mitigation plan.
Research Goals and Benefits
Develop intelligent transportation infrastructure monitoring and predictive maintenance algorithms
Enhance rail safety, efficiency, and infrastructure resilience.
Enable data-driven decision-making for cost-effective capital investments.
Promote climate resilience, and reliable multimodality transport in Rail, Road, Air, and more.
Yili (Kelly) Tang, Ph.D., P.Eng
Assistant Professor
Tanjian Wei
Ph.D.
Postdoctoral associate in Western
Suryakant Buchunde
PhD research student
Duha Abdallah
MESc research student
Zelin Zhang
MESc research student
Industrial partners