Title:
Incorporating Travel Behaviors into Transit Network Designs: Methods, Applications, and Extensions
Incorporating Travel Behaviors into Transit Network Designs: Methods, Applications, and Extensions
Author(s)
Guan, Hongzhao
Advisor(s)
Van Hentenryck, Pascal
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Over the past few decades, urban areas have witnessed consistent population growth, leading to a significant rise in privately owned vehicles. Consequently, there's a growing need for new transit systems to address these challenges effectively. 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. Advanced machine learning models are commonly employed for modeling demand, while optimization frameworks with fixed demand are typically used for network design problems. 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 3 extends the concept of ODMTS-DA to Transit Network Design with Adoptions (TN-DA) and designs heuristic algorithms to solve the problem efficiently. The chapter also provides guideline metrics for transit agencies and conducts extensive large-scale case studies on different transit systems.
Lastly, Chapter 5 applies the concepts introduced in earlier chapters to a different domain—public school redistricting. It proposed a Contextual Stochastic Optimization framework and applies it to study the impact of redrawing elementary school attendance boundaries on socioeconomic segregation. Computational results reveal the effectiveness of the framework in predicting school choice and its potential to reduce segregation in schools, offering valuable insights for policymakers and academics.
Sponsor
Date Issued
2024-12-02
Extent
Resource Type
Text
Resource Subtype
Dissertation