Yang Liu
Dr. Yang Liu is a Marie Curie Fellow at Chalmers University of Technology. He serves as a Young Editor for two top-tier academic journals, including the Innovation (Cell Press’ flagship journal, Impact factor = 32.1) and IEEE/CAA Journal of Automatica Sinica (Impact factor = 11.8). In addition, he is an Associate Editor of IEEE Transactions on Intelligent Vehicles (Impact factor = 8.2) and Journal of Intelligent and Connected Vehicles. Dr. Yang Liu leads a European Union project and a project funded by the Swedish Innovation Agency. His work has been published in top international journals in the fields of intelligent transportation and automation, such as The Innovation, IEEE TCYB, IEEE TITS, IEEE TIV, IEEE/CAA JAS, IEEE ITSM, and CACAIE Transportation Research Part A/C/E. Dr. Yang Liu has received numerous awards for his research, including the IEEE ITSM Best Paper High Commendation Award, Honorable Mention of the COTA Best Dissertation Award, ICME Grand Challenge Second Runner-up Award, and the China Highway Society Outstanding Doctoral Dissertation Award. He is experienced in the practice of AI techniques and has won several world prizes in AI competitions organized by leading international AI conferences or research institutes (e.g., KDD, IJCAI, NeurIPS, CVPR, ICME, TRB), including the 1st place of KDD Cup, the most well-known algorithm competition in data mining.
Showing 24 publications
An overview of solutions to the bus bunching problem in urban bus systems
A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
Envisioning the future of transportation: Inspiration of ChatGPT and large models
A spatio-temporal deep learning model for short-term bike-sharing demand prediction
Prospects of eVTOL and Modular Flying Cars in China Urban Settings
How machine learning informs ride-hailing services: A survey
Personalized Modeling of Travel Behaviors and Traffic Dynamics
Model Controlled Prediction: A Reciprocal Alternative of Model Predictive Control
A personalized recommendation system for multi-modal transportation systems
DeepTSP: Deep traffic state prediction model based on large-scale empirical data
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Showing 10 research projects
Digital solutions for sustainable planning and management of shared micromobility using Big Data
Electric Multimodal Transport Systems for Enhancing Urban Accessibility and Connectivity (eMATS)
Applied AI for Connected and Autonomous Transportation Systems
Analyzing and promoting micro-shared mobility system leveraging big data
ATEM - Accelerating transport electrification by machine learning
Battery Ageing Prediction and Optimization in a Fleet of Electric Autonomous Vehicles
Accelerating transport electrification by machine learning
ICV-Safe: Testing safety of intelligent connected vehicles in open and mixed road environment