Xiaoyan Xie - RESEARCH

Research for urban Taffic & Mobility

Xiaoyan's research interests mainly focused on study and modeling of emerging multimodal (road, rail, sea or air) and monodal passenger transportation systems, new 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 for bi-modal traffic (vehicle and passenger - MFD and pMFD); bottleneck models (bus, metro); traffic control (traffic signals and lane); 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 pasengers and vehicles; "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).

BLIP (Bus Lanes with Intermittent Priority)

The goals were to gain insight into the characteristics of traffic, demand of transportation, travel behaviors, mobility patterns, intermodal facility operations and urban dynamics to solve inherent and novel issues, such as congestion, accessibility, safty, risk, spatial structure, etc. The ultimate objectives were to enhance the Quality of Service (QoS) and the land use to promote the planning and development of eco-friendly, comfortable, safe, and sustainable transportation and mobility systems.

The above studies involved multiple data sources of different cities or regions (Greater Paris, Shanghai, Dalian, etc.), such as synthetic data, new passive massive datasets - Big Data of ITS systems (AFC/smart ticketing, FCD, GPS/AVL, Smartphone, etc.), OpenData (AFC, GTFS, OSM, etc.), and transport surveys (EGT, RP, Original-designed sampling survey). They were complied with the GDPR and intellectual property protections and funded by projects, funds, fellowships or grants which were supported by our institutions.

She founded a smart mobility research group called SmartMobLab.

Keywords: Traffic flow theory; Traffic control and operation; Network modeling; Demand modeling; (Digital) Mobility analytics; Transport econometrics; Machine learning; Simulation; Optimization; Automation and Intelligence; Transportation systems; Public transportation