Yuan Liao
Yuan Liao works in the field of intelligent transportation systems. Her research focuses on the understanding of human mobility pattern. Big and continuous data from newly emerged sources, such as Twitter and mobile phone, provide opportunities to gain new insights. How could those data be utilized to characterize mobility patterns of city and citizens? How could we apply such understanding to benefit the environmental sustainability in cities?
Her active research interests include intelligent transportation, mobility patterns, and vehicular human factors. She received the Excellent Graduate Student and Excellent Master Thesis of the Year from Tsinghua University in July 2016.
Showing 23 publications
A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns
Impacts of charging behavior on BEV charging infrastructure needs and energy use
Synthetic Sweden Mobility (SySMo) Model Documentation
A Mobility Model for Synthetic Travel Demand from Sparse Traces
Feasibility of estimating travel demand using geolocations of social media data
Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016
Ride-sourcing compared to its public-transit alternative using big trip data
How drivers respond to visual vs. auditory information in advisory traffic information systems
Disparities in travel times between car and transit: Spatiotemporal patterns in cities
Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios
Predictability in Human Mobility based on Geographical-boundary-free and Long-time Social Media Data
Context-Adaptive support information for truck drivers: An interview study on its contents priority
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Showing 3 research projects
Electric Multimodal Transport Systems for Enhancing Urban Accessibility and Connectivity (eMATS)