Models and large-scale coordination algorithms for autonomous mobility-on-demand
Abstract/Contents
- Abstract
- Urban mobility in the 21st century faces significant challenges, as the unsustainable trends of urban population growth, congestion, pollution, and low vehicle utilization worsen in large cities around the world. As autonomous vehicle technology draws closer to realization, a solution is beginning to emerge in the form of autonomous mobility-on-demand (AMoD), whereby fleets of self-driving vehicles transport customers within an urban environment. This dissertation introduces a systematic approach to the design, control, and evaluation of these systems. In the first part of the dissertation, a stochastic queueing-theoretical model of AMoD is developed, which allows both the analysis of quality-of-service metrics as well as the synthesis of control policies. This model is then extended to one-way car sharing systems, or human-driven mobility-on-demand (MoD) systems. Based on these models, closed-loop control algorithms are designed to efficiently route empty (rebalancing) vehicles in very large systems with thousands of vehicles. The performance of the algorithms and the potential societal benefits of AMoD and MoD are evaluated through case studies of New York City and Singapore using real-world data. In the second part of the dissertation, additional structural and operational constraints are considered for AMoD systems. First, the impact of AMoD on traffic congestion with respect to the underlying structural properties of the road network is analyzed using a network flow model. In particular, it is shown that empty rebalancing vehicles in AMoD systems will not increase congestion, in stark contrast to popular belief. Finally, the control of AMoD systems with additional operational constraints is studied under a model predictive control framework, with a focus on range and charging constraints of electric vehicles. The technical approach developed in this dissertation allows us to evaluate the societal benefits of AMoD systems as well as lays the foundation for the design and control of future urban transportation networks.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Zhang, Rick |
|---|---|
| Associated with | Stanford University, Department of Aeronautics and Astronautics. |
| Primary advisor | Pavone, Marco, 1980- |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Rock, Stephen M |
| Thesis advisor | Schwager, Mac |
| Advisor | Rock, Stephen M |
| Advisor | Schwager, Mac |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Rick Zhang. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Rick Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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