Material Type  

 학위논문

 

0017162512

Date and Time of Latest Transaction  

20250211152021

ISBN  

9798383221167

DDC  

385

Author  

Shang, Mingfeng.

Title/Author  

Enabling the Next Generation of Transportation Systems by Accounting for Heterogeneity in Traffic Flow: Modeling and Control of Mixed Autonomy Traffic.

Publish Info  

[S.l.] : University of Minnesota., 2024

Publish Info  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Material Info  

319 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

General Note  

Advisor: Stern, Raphael E.

학위논문주기  

Thesis (Ph.D.)--University of Minnesota, 2024.

Abstracts/Etc  

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).

Subject Added Entry-Topical Term  

Transportation.

Subject Added Entry-Topical Term  

Engineering.

Subject Added Entry-Topical Term  

Automotive engineering.

Subject Added Entry-Topical Term  

Urban planning.

Subject Added Entry-Topical Term  

Robotics.

Index Term-Uncontrolled  

Adaptive cruise control

Index Term-Uncontrolled  

Traffic control

Index Term-Uncontrolled  

Traffic modeling and simulation

Index Term-Uncontrolled  

Automated vehicles

Added Entry-Corporate Name  

University of Minnesota Civil Engineering

Host Item Entry  

Dissertations Abstracts International. 86-01B.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:653946