Project 1: Chicago Taxi OD Flow
Dynamic visualization
Project 2: Crowdsourcing data Analytics
This project will uncover the performance and efficiency of shared systems drawing on extensive crowd-sourced data and innovative data analytics and models. We attempt to solve the problems, such as how do collaborative system promoters (websites, blogs) convey the expected impacts of collaborative consumption to the public? How do consumers make sense of their own use of collaborative consumption? How does the peer-to-peer matching underpinning collaborative consumption systems function?
Project 3: Commuter Ride-Sharing Using Topology-Based Vehicle Trajectory Clustering
A new data-driven ride-matching method is presented. It tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters.
Project 4: A Spatio-Temporal Vehicle Trajectory Clustering Algorithm (ST-TOPOSCAN)
We present a spatio-temporal trajectory clustering method for vehicle trajectories in transportation networks to identify heterogeneous trip patterns and explore underlying network assignment mechanisms.