Events Calendar

PhD Dissertation Defense - Haowen Li

Title: Controllable Trajectory Generation  
 
Date and Time: Monday, August 5th, 2024: 1:00p -3:00p   
 
Location: Ronald Tutor Hall of Engineering (RTH) - 306        
 
Committee Members: Prof. Cyrus Shahabi (Chair), Prof. Bistra Dilkina, Prof. Marlon Boarnet and Prof. Xiong Li
 
Abstract: Accessing realistic human movements (aka trajectories) is essential for many application domains, such as urban planning, transportation, and public health (e.g., understanding the spread of an epidemic). However, 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. Traditional rule-based methods rely on predefined heuristics and distributions that fail to capture complicated transition patterns in human mobility. Inspired by the success of deep neural networks (DNN), data-driven methods learn underlying human decision-making mechanisms and generate synthetic trajectories by directly fitting real-world data. Despite this progress, existing approaches lack mechanisms to control the generation process, which prevents the incorporation of prior knowledge and the spatiotemporal specification of certain visits. This lack of control on the generated trajectories greatly limits their practical applicability. In addition, existing studies on trajectory mining applications often project GPS coordinates onto discrete geographical grids and time intervals. However, modeling human movements requires algorithms that can effectively capture inherently complex spatial and temporal dependencies and transforming trajectories into regular grids and time intervals cannot accurately model real-world trajectories with irregular moving patterns. This thesis addresses the above two shortcomings and proposed generation algorithms under various control settings.    
 
In this thesis defense, I will present my work on controllable trajectory generation. I will first provide the motivations and review previous work on implicitly controlled trajectory generation via clustering. Subsequently, I will formally define the Constraint Trajectory Generation problem and our framework that operates within continuous spatiotemporal space, enabling the direct generation of geographical coordinates and the duration of each visit in a trajectory. In conclusion, I will discuss future directions for the development of trajectory generation models  
 
Zoom Link:  https://usc.zoom.us/j/98507965284?pwd=Kbx7MCqHVizVGOnTcgxLwQs04qe8Aa.1
 
Password: 1234

Location: Ronald Tutor Hall of Engineering (RTH) - 306

Audiences: Everyone Is Invited

Contact: Haowen Li

Event Link: https://usc.zoom.us/j/98507965284?pwd=Kbx7MCqHVizVGOnTcgxLwQs04qe8Aa.1