Intelligent Transportation Track
The Intelligent Transportation track aims to explore the advanced techniques, theory, applications, simulations, and architectures of the next generation transportation systems. With the help of emerging artificial intelligence and big data processing techniques, the next generation transportation systems should have good opportunities to overcome the challenges and obstacles in the past. The topics of this track include, but are not limited to, traffic operation theory, driver behaviour, traffic data management, autonomous driving, location-based services, traffic simulation, and edge computing. Our track particularly invites and encourages prospective speakers to share their work, findings, perspectives and developments as related to implementation and deployment of advanced intelligent transportation systems and applications.
Invited Speakers (in alphabetical order)
Yang CAO
Associate Professor
Tokyo Institute of Technology, Japan
Talk Title: Attacks and Defenses in Spatiotemporal Federated Learning
Tung KIEU
Assistant Professor
Aalborg University, Denmark
Talk Title: Intelligent Transportation
Hao LIU
Assistant Professor
The Hong Kong University of Science and Technology (Guangzhou), China
Talk Title: Urban Foundation Model: Concepts, Challenges, and A Unified Framework
Jianzhong QI
Associate Professor
The University of Melbourne, Australia
Talk Title: Traffic Forecasting for Regions without Historical Observations
Jianzhong Qi is an Associate Professor in the School of Computing and Information Systems, The University of Melbourne, Australia. His general research area is data management, and his research concerns fundamental algorithms for spatial, temporal, and geo-textual data, including data indexing, query and update processing, and machine learning.
Kelly Yili TANG
Assistant Professor
Western University, Canada
Talk Title: Crowding Management and Dynamic Incentives For Urban Commuting
Xun ZHOU
Professor
Harbin Institute of Technology, Shenzhen, China
Talk Title: Enhancing the Generalizability of Spatiotemporal Intelligence Models for Robust Traffic Accident Forecasting