Techniques for efficient and responsible operation of mobility systems
Abstract/Contents
- Abstract
- Transportation is a necessary resource for many societies around the world. While advances in data science provide promising tools for personalized, adaptive and more efficient mobility services, they also bring new challenges in equal measure. In this dissertation I will discuss algorithm design for two such challenges faced by modern mobility services. First, I will discuss techniques for operating ridehailing and ridesharing systems in settings with incomplete information, which often arise due to the on-demand nature of such services. In particular, I will show both in theory and in practice how ideas from model predictive control, online optimization and machine learning can be used to effective serve existing customers while also adequately preparing for unknown future demand. Second, I will highlight some privacy concerns that arise from the sharing of mobility data that is often required for modern data-driven algorithms. To address some of these concerns, I present techniques based on multiparty computation and differential privacy to effectively use location data to improve routing services in a privacy-preserving way.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tsao, Matthew Wu |
|---|---|
| Degree supervisor | Pavone, Marco, 1980- |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Sadigh, Dorsa |
| Degree committee member | Boyd, Stephen P |
| Degree committee member | Sadigh, Dorsa |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Matthew Wu Tsao. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/cq614xb8649 |
Access conditions
- Copyright
- © 2022 by Matthew Wu Tsao
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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