PhD Thesis Proposal - Haowen Lin

Committee: Cyrus Shahabi (Chair), Bistra Dilkina, Muhao Chen, Xiong Li, Marlon Boarnet
 
Title: Accurate and controllable trajectory generation  
 
Abstract : In various application domains like transportation, urban planning, and public health, analyzing human mobility, represented as a sequence of consecutive visits (aka trajectories), is crucial for uncovering essential mobility patterns. Due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. This thesis addresses the challenge of trajectory generation using data-driven approaches, integrating both explicit and implicit constraints within a continuous spatiotemporal domain. First, I present a framework based on generative adversarial imitation learning that synthesizes realistic trajectories that preserve moving behavior patterns (.g., work commute, shopping purpose) in real data. Next, I explore the hypothesis that grouping trajectories governed by similar dynamics into clusters before trajectory modeling could enhance modeling effectiveness. I present a framework that can simultaneously model trajectories in continuous space and time  while clustering them. Finally, we discuss the proposed work that will incorporate explicit spatial and temporal constraints that will potentially generate more representative and realistic trajectories.  
 
Zoom link: https://usc.zoom.us/j/95828555243

Location: https://usc.zoom.us/j/95828555243

Audiences: Everyone Is Invited

Contact: CS Events

Event Link: zoom link: https://usc.zoom.us/j/95828555243