東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Models and Large-Scale Coordination ...

Zhang, Rick.

Linked to FindBook

Google Book

Amazon

博客來

Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand./
Author:	Zhang, Rick.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	150 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
Subject:	Systems science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28118542
ISBN:	9798662549821

Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand.
Zhang, Rick.

Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 150 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--Stanford University, 2016.

This item must not be sold to any third party vendors.

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.

ISBN: 9798662549821Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Autonomous vehicles

Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand.
LDR:03268nmm a2200373 4500 001 2276046
005 20210416102021.5
008 220723s2016 ||||||||||||||||| ||eng d
020 $a 9798662549821
035 $a (MiAaPQ)AAI28118542
035 $a (MiAaPQ)STANFORDzq681vs1170
035 $a AAI28118542
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Rick. $3 3554302
245 1 0 $a Models and Large-Scale Coordination Algorithms for Autonomous Mobility-On-Demand.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 150 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
500 $a Advisor: Pavone, Marco; Rock, Stephen M.;Schwager, Mac.
502 $a Thesis (Ph.D.)--Stanford University, 2016.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0212.
650 4 $a Systems science. $3 3168411
650 4 $a Automotive engineering. $3 2181195
650 4 $a Transportation. $3 555912
650 4 $a Mechanical engineering. $3 649730
653 $a Autonomous vehicles
653 $a self-driving vehicles
653 $a transportation networks
690 $a 0548
690 $a 0709
690 $a 0540
690 $a 0790
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 82-04B.
790 $a 0212
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28118542