Leveraging Connected and Automated Vehicles for Participatory Traffic Control

Wu, Minghui; Wang, Xingmin; Yin, Yafeng; Liu, Henry; Wang, Ben; Lynch, Jerome P

doi:10.7302/8088

i

Leveraging Connected and Automated Vehicles for Participatory Traffic Control

Supporting Files

2023-08-01
By Wu, Minghui ; Wang, Xingmin ; Yin, Yafeng ; ...

File Language:

English

Details

Creators:

Wu, Minghui ; Wang, Xingmin ; Yin, Yafeng ; Liu, Henry ; Wang, Ben ; Lynch, Jerome P
Corporate Creators:

Center for Connected and Automated Transportation (CCAT) ; Michigan State University
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:

Autonomous Vehicles Connected Vehicles Intelligent Vehicles Traffic Control Traffic Estimation Mean-field Control
Publication/ Report Number:

CCAT Report No. 56
DOI:

https://doi.org/10.7302/8088
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report (February 2021–August 2023)
Corporate Publisher:

University of Michigan. Center for Connected and Automated Transportation
Abstract:

This report lays the theoretical groundwork for participatory traffic control that integrates traditional infrastructure like traffic signals with connected and automated vehicles (CAVs) acting as mobile actuators. The study is divided into two main parts. First, we introduce a robust traffic state estimation method that leverages real-time data from CAVs to yield precise insights into the transportation system. Second, we present an analytical framework to guide the strategic control of CAVs, aiming to indirectly influence the behavior of human-driven vehicles and optimize traffic flow across various time periods and transportation facilities. Our research serves as a foundational step towards the practical deployment of participatory traffic control systems, contributing to the creation of smarter, more efficient transportation networks for the future.
Format:

PDF
Funding:

69A3551747105
Collection(s):

Autonomous Vehicles University Transportation Centers
Main Document Checksum:

urn:sha-512:949cb093c1f027e70dd1a4d93dcf40aed8e32f50cf1893b2c5f87078008a4ba04c1dbc9e8456772f9ed95cc35de9796085b1b5dcae1d7d7375a0efd0ff655be7
Download URL:

https://rosap.ntl.bts.gov/view/dot/68873/dot_68873_DS1.pdf
File Type:

[PDF - 5.93 MB ]

Multiday_User_Equilibrium_with_Intelligent_Traveler_202308_REM.pdf

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File Language:

English

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