Research Areas

Travel Behavior Modeling


This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.


Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.


Understanding and expressing public transit system utilization based on fundamental travel behavior.


Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.


The project suggests a bottom-up travel behavior driven approach which obtains trends in individual travel behavior first and use such information to enhance longitudinal origin-destination demand monitoring.


Integrating machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data.