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Coordinated Transportation Systems: From Grid-Savvy Routing For Electric Trucks To Stochastic Control Of Demand-Responsive Vehicles
Coordinated Transportation Systems: From Grid-Savvy Routing For Electric Trucks To Stochastic Control Of Demand-Responsive Vehicles
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151351
- ISBN
- 9798382590776
- DDC
- 628
- 서명/저자
- Coordinated Transportation Systems: From Grid-Savvy Routing For Electric Trucks To Stochastic Control Of Demand-Responsive Vehicles
- 발행사항
- [Sl] : Carnegie Mellon University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 226 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Pozzi, Matteo.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2024.
- 초록/해제
- 요약The coordination of vehicles for the movement of goods and people is crucial for achieving economically viable, sustainable, and equitable transportation systems. This thesis explores two settings of optimally planning and controlling fleets of vehicles: one bridges the gap in electrifying delivery services, while the other enables demand-responsive and equitable public transportation. Focusing first on the economic viability of grid-savvy electric delivery fleets, I develop planning algorithms that optimize fleet design, route planning, and managed charging. This addresses the technological constraints limiting the adoption of electrified medium- and heavy-duty vehicles (MHDVs) that make them fundamentally different to operate than their conventional fossil-fueled counterparts. I extend the electric vehicle routing problem to jointly optimize routing and vehicle-to-grid interactions. Through regional case studies, I demonstrate that EVs can become cost-competitive with diesel trucks when coordinating delivery routes and charging under time-varying commercial electricity rates. Furthermore, EVs with vehicle-to-grid (V2G) capabilities can reduce peak demand charges and exploit price differences between utilities, resulting in net negative costs in some cases. The second setting in this dissertation demonstrates how optimal coordination of a demand-responsive vehicle with shared transportation provides service quality and equity improvements in passenger transportation. First, I propose a sequential decision-making problem in which a demand-responsive vehicle selects actions to coordinate the service of pooled ride requests on routes with shared transit to maximize ridership, reduce travel times, and increase service quality while considering costs and equity preferences specified by a centralized transit agency. Several heuristic methods, exact dynamic programming, and reinforcement learning methods are investigated to optimally control the demand-responsive vehicle. Through a New York City case study, I demonstrate the feasibility of each method for operating a demand-responsive vehicle that augments shared transit to maximize equity and service quality metrics. The results validate the viability of reinforcement learning approaches compared to heuristic policies at realistic scales. The contributions of this thesis provide insights into the operational challenges of coordinating fleets of vehicles across different transportation domains and offer algorithmic solutions to enable more economically viable and equitable mobility systems.
- 키워드
- Vehicle-to-grid
- 기타저자
- Carnegie Mellon University Civil and Environmental Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
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■020 ▼a9798382590776
■035 ▼a(MiAaPQ)AAI31243192
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a628
■1001 ▼aAriss, Rami Samir.▼0(orcid)0000-0003-3317-7390
■24510▼aCoordinated Transportation Systems: From Grid-Savvy Routing For Electric Trucks To Stochastic Control Of Demand-Responsive Vehicles
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a226 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Pozzi, Matteo.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2024.
■520 ▼aThe coordination of vehicles for the movement of goods and people is crucial for achieving economically viable, sustainable, and equitable transportation systems. This thesis explores two settings of optimally planning and controlling fleets of vehicles: one bridges the gap in electrifying delivery services, while the other enables demand-responsive and equitable public transportation. Focusing first on the economic viability of grid-savvy electric delivery fleets, I develop planning algorithms that optimize fleet design, route planning, and managed charging. This addresses the technological constraints limiting the adoption of electrified medium- and heavy-duty vehicles (MHDVs) that make them fundamentally different to operate than their conventional fossil-fueled counterparts. I extend the electric vehicle routing problem to jointly optimize routing and vehicle-to-grid interactions. Through regional case studies, I demonstrate that EVs can become cost-competitive with diesel trucks when coordinating delivery routes and charging under time-varying commercial electricity rates. Furthermore, EVs with vehicle-to-grid (V2G) capabilities can reduce peak demand charges and exploit price differences between utilities, resulting in net negative costs in some cases. The second setting in this dissertation demonstrates how optimal coordination of a demand-responsive vehicle with shared transportation provides service quality and equity improvements in passenger transportation. First, I propose a sequential decision-making problem in which a demand-responsive vehicle selects actions to coordinate the service of pooled ride requests on routes with shared transit to maximize ridership, reduce travel times, and increase service quality while considering costs and equity preferences specified by a centralized transit agency. Several heuristic methods, exact dynamic programming, and reinforcement learning methods are investigated to optimally control the demand-responsive vehicle. Through a New York City case study, I demonstrate the feasibility of each method for operating a demand-responsive vehicle that augments shared transit to maximize equity and service quality metrics. The results validate the viability of reinforcement learning approaches compared to heuristic policies at realistic scales. The contributions of this thesis provide insights into the operational challenges of coordinating fleets of vehicles across different transportation domains and offer algorithmic solutions to enable more economically viable and equitable mobility systems.
■590 ▼aSchool code: 0041.
■650 4▼aEnvironmental engineering
■653 ▼aCoordinated transportation
■653 ▼aDemand-responsive transportation
■653 ▼aElectric vehicle routing problem
■653 ▼aEquitable ride-pooling
■653 ▼aReinforcement learning
■653 ▼aVehicle-to-grid
■690 ▼a0543
■690 ▼a0796
■690 ▼a0800
■690 ▼a0775
■71020▼aCarnegie Mellon University▼bCivil and Environmental Engineering.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161397▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
