You are cordially invited to my thesis defense on August 19th.

Title: Incorporating Travel Behaviors into Transit Network Designs: Methods, Applications, and Extensions

Date: Monday, August 19, 2024

Time: 9:00 am – 11:00 am ET

Location:

CODA C1115 Druid Hills

Or
MS Teams
 

Hongzhao Guan

Machine Learning PhD Student

H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee

1 Dr. Pascal Van Hentenryck (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

2 Dr. Benoit Montruil, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

3 Dr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

4 Dr. Subhrajit Guhathakurta, School of City and Regional Planning, Georgia Institute of Technology

5 Dr. Beste Basciftci, Department of Business Analytics, University of Iowa

Abstract:
Over the past few decades, urban areas have witnessed consistent population growth, leading to a significant rise in privately owned vehicles. Investment in public transit is often expected to make positive impacts on challenges such as traffic congestion and air quality. One such promising solution is On-Demand Multimodal Transit Systems (ODMTS) which integrate on-demand shuttles with fixed transit services to provide cost-effective and convenient transportation options. Chapter 2 investigates ODMTS from two crucial perspectives: network design and demand modeling. The chapter also discusses real-world ODMTS deployments, such as MARTA Reach in 2022 and CAT Smart in 2024. Chapter 3 studies on the ODMTS Design with Adoptions (ODMTS-DA) problem, aiming to incorporate choice models into optimization frameworks to handle latent demand while designing ODMTS. It proposes a path-based optimization model called P-PATH to address computational difficulties, achieving significant computational improvements compared to existing approaches. Similarly, Chapter 4 extends the concept of ODMTS-DA to Transit Network Design with Adoptions (TN-DA) and designs heuristic algorithms to solve the problem efficiently. Lastly, Chapter 5 applies the concepts introduced in earlier chapters to a different domain—public school redistricting. It develops a joint redistricting and choice modeling framework and applies it to study the impact of redrawing elementary school attendance boundaries on socioeconomic segregation.