Research for Urban Taffic & Mobility  (uTRAM)

Xiaoyan's research interests mainly focused on study and modeling of emerging passenger transportation systems, new human mobility services, or related new data; and related theories based on physics, traffic flow theory, operations research methods, artificial intelligence approaches, including

• Physics-based multimodal or intermodal transportation system models: Network traffic modeling; traffic flow theory of vehicle and passenger - MFD and pMFD of bi-modal traffic; bottleneck models (bus, metro); traffic control (traffic signals, lane operation); design - modeling - simulation of emerging passenger transportation systems with effective strategies for managing bus operations (BLIP vs. DBL, OBL), mixed traffic of new services (EV, AV), or passenger intermodal facilities (mass transit hub, P&R, port);

• Physical-stochastic demand models for public transportation: Physical-stochastic modeling of passenger flow; O-D estimation; passenger in-station behaviors (metro, train); data-driven dynamic transit assignment modeling; 

• AI based data-driven models for urban mobility analytcs and land use: Activity trip-chain modeling for pasenger and vehicle; "AI+” data-driven modeling based on multiple data sources, such as synthetic data, new passive massive data - Big Data of ITS, OpenData, and travel surveys; mobility patterns; urban dynamics; passenger safety (COVID-19 impact).