Development of a comprehensive digital twin framework for drivers, integrating real-time driving data, surrounding environment, and AI-driven prediction based onpersonalized driving style .
Development of an advanced traffic demand modeling system using deep learning to synthesize human mobility patterns in urban environments.
Interactive simulation platform enabling multiple users to participate in real-time traffic scenarios, providing valuable insights into human behavior in complex traffic situations.
Development of an advanced vehicle digital twin system integrating real-time vehicle dynamics and environmental interactions for enhanced mobility research.
Data science for traffic dataset fusion. Advanced AI-driven system for modeling and predicting traffic patterns around work zones and incidents, enhancing safety and traffic flow efficiency.
Development of intelligent transportation systems leveraging knowledge graphs and machine learning for enhanced decision-making and traffic management.
Research on understanding and modeling driver behavior patterns, incorporating personality traits, emotional states, and driving styles to enhance transportation system safety and efficiency.