서브메뉴
검색
Enabling the Next Generation of Transportation Systems by Accounting for Heterogeneity in Traffic Flow: Modeling and Control of Mixed Autonomy Traffic
Enabling the Next Generation of Transportation Systems by Accounting for Heterogeneity in Traffic Flow: Modeling and Control of Mixed Autonomy Traffic
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152021
- ISBN
- 9798383221167
- DDC
- 385
- 저자명
- Shang, Mingfeng.
- 서명/저자
- Enabling the Next Generation of Transportation Systems by Accounting for Heterogeneity in Traffic Flow: Modeling and Control of Mixed Autonomy Traffic
- 발행사항
- [Sl] : University of Minnesota, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 319 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Stern, Raphael E.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2024.
- 초록/해제
- 요약Traffic engineering is a field characterized by heterogeneity, reflecting the diverse behaviors of individual agents using the infrastructure. While traffic heterogeneity has been discussed for several decades, primarily focusing on vehicle types and human driver behavior, recent years have seen an expansion of the conversation to include emerging technologies like automated and electric vehicles. This heterogeneity impacts driving behavior such that even a single agent's actions can substantially influence local traffic flow. Motivated by such findings, this dissertation focuses on understanding and investigating the heterogeneity of driving behavior in traffic flow, particularly the intrinsic differences between autonomous driving and human driving and their impacts on system-level dynamics. The goal is to leverage these differences to improve overall performance, sustainability, and resilience. From the standpoint of traffic flow, altering the behavior of even a small number of individual agents can have significant implications for the emergent properties of the entire flow, such as passenger travel time and vehicle energy consumption. If properly controlled, certain desirable properties of traffic flow, such as stability and increased highway throughput, can be achieved. The spectrum of vehicle automation spans from SAE Level 1 - comprising partially automated vehicles equipped with driver-assist functions like adaptive cruise control (ACC) - to SAE Level 5, which refers to fully automated vehicles (AVs) that operate without any human intervention. While numerous benefits have been demonstrated for fully automated traffic flow, it remains unclear how partially automated vehicles, which are already commercially available, will influence mixed autonomy traffic characteristics in the near future. Therefore, this dissertation aims to develop methodological tools for the mathematical modeling, simulation, and control of mixed autonomy traffic involving fully automated, partially automated, and human-driven vehicles.This dissertation fits into three distinct thrusts: i) developing physically interpretable car-following models to accurately describe the dynamics of ACC vehicles and human-driven vehicles (Chapters 2, 3, and 4); ii) investigating how commercially available ACC vehicles will impact mixed autonomy traffic with human-driven vehicles at different market penetration rates (Chapters 5 and 6); iii) designing and controlling next-generation intelligent infrastructure and vehicles to better adapt to mixed autonomy traffic (Chapters 7, 8, and 9).
- 일반주제명
- Transportation
- 일반주제명
- Engineering
- 일반주제명
- Automotive engineering
- 일반주제명
- Urban planning
- 일반주제명
- Robotics
- 키워드
- Traffic control
- 기타저자
- University of Minnesota Civil Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017162512
■00520250211152021
■006m o d
■007cr#unu||||||||
■020 ▼a9798383221167
■035 ▼a(MiAaPQ)AAI31332294
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a385
■1001 ▼aShang, Mingfeng.
■24510▼aEnabling the Next Generation of Transportation Systems by Accounting for Heterogeneity in Traffic Flow: Modeling and Control of Mixed Autonomy Traffic
■260 ▼a[Sl]▼bUniversity of Minnesota▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a319 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Stern, Raphael E.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2024.
■520 ▼aTraffic engineering is a field characterized by heterogeneity, reflecting the diverse behaviors of individual agents using the infrastructure. While traffic heterogeneity has been discussed for several decades, primarily focusing on vehicle types and human driver behavior, recent years have seen an expansion of the conversation to include emerging technologies like automated and electric vehicles. This heterogeneity impacts driving behavior such that even a single agent's actions can substantially influence local traffic flow. Motivated by such findings, this dissertation focuses on understanding and investigating the heterogeneity of driving behavior in traffic flow, particularly the intrinsic differences between autonomous driving and human driving and their impacts on system-level dynamics. The goal is to leverage these differences to improve overall performance, sustainability, and resilience. From the standpoint of traffic flow, altering the behavior of even a small number of individual agents can have significant implications for the emergent properties of the entire flow, such as passenger travel time and vehicle energy consumption. If properly controlled, certain desirable properties of traffic flow, such as stability and increased highway throughput, can be achieved. The spectrum of vehicle automation spans from SAE Level 1 - comprising partially automated vehicles equipped with driver-assist functions like adaptive cruise control (ACC) - to SAE Level 5, which refers to fully automated vehicles (AVs) that operate without any human intervention. While numerous benefits have been demonstrated for fully automated traffic flow, it remains unclear how partially automated vehicles, which are already commercially available, will influence mixed autonomy traffic characteristics in the near future. Therefore, this dissertation aims to develop methodological tools for the mathematical modeling, simulation, and control of mixed autonomy traffic involving fully automated, partially automated, and human-driven vehicles.This dissertation fits into three distinct thrusts: i) developing physically interpretable car-following models to accurately describe the dynamics of ACC vehicles and human-driven vehicles (Chapters 2, 3, and 4); ii) investigating how commercially available ACC vehicles will impact mixed autonomy traffic with human-driven vehicles at different market penetration rates (Chapters 5 and 6); iii) designing and controlling next-generation intelligent infrastructure and vehicles to better adapt to mixed autonomy traffic (Chapters 7, 8, and 9).
■590 ▼aSchool code: 0130.
■650 4▼aTransportation
■650 4▼aEngineering
■650 4▼aAutomotive engineering
■650 4▼aUrban planning
■650 4▼aRobotics
■653 ▼aAdaptive cruise control
■653 ▼aTraffic control
■653 ▼aTraffic modeling and simulation
■653 ▼aAutomated vehicles
■690 ▼a0709
■690 ▼a0999
■690 ▼a0537
■690 ▼a0540
■690 ▼a0771
■71020▼aUniversity of Minnesota▼bCivil Engineering.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162512▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
