Exploiting shared structures in large GPS trajectory datasets under uncertainty
Abstract/Contents
- Abstract
- As GPS-enabled devices become ubiquitous, large collections of GPS trajectories have been used for mobility pattern mining and intelligent transportation applications, such as suggesting routes and predicting traffic jams. Compared to traditional data sources, such as traffic sensors, cameras and surveys, trajectory data from moving vehicles have the advantage of being dynamic, cheap, and highly available. Thus they allow true data-driven solutions to many problems that are traditionally solved using modeling and simulation approaches. On the other hand, the majority of available trajectory data contain a large amount of uncertainty, due to GPS noise, low sampling rates and missing data. Such uncertainty can significantly degrade the effectiveness of using large trajectory data in practice. Conventional methods for reducing uncertainty in trajectory data, such as map matching and trajectory interpolation tend to process each trajectory independently, without considering the shared structures in large trajectory data that can improve the processing of individual trajectories. In this dissertation, we present three algorithms that exploit shared structures in large trajectory collections to reduce noise and sample sparsity, and to improve trajectory-based travel time prediction under the difficult scenario of having far less GPS-tracking units than normally required in previous studies. These works make use of different kinds of shared structures, such as popular routes on a road map, trajectory clusters that represent unique traffic flows across trajectory junctions, and recurring traffic patterns over a small neighborhood. They also bring novel insights on how to extract robust knowledge from uncertain data, and how to effectively incorporate learned knowledge to individual trajectory processing tasks.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Li, Yang |
|---|---|
| Associated with | Stanford University, Computer Science Department. |
| Primary advisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Williams Vassilevska, Virginia, 1980- |
| Advisor | Kochenderfer, Mykel J, 1980- |
| Advisor | Williams Vassilevska, Virginia, 1980- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yang Li. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Yang Li
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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