Center for Connected and Automated Transportation > Research > Research from the University of Michigan > Trajectory Based Traffic Control with Low Penetration of Connected and Automated Vehicles

Trajectory Based Traffic Control with Low Penetration of Connected and Automated Vehicles

Trajectory Based Traffic Control with Low Penetration of Connected and Automated Vehicles

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Headshot of Yiheng Feng. The link directs to their bio page.
Yiheng Feng
Headshot of Henry Liu. The link directs to their bio page.
Henry Liu
The University of Michigan Transportation Research Institute Logo. The link directs to the funded research led by this institution.

Principal Investigator(s):

Yiheng Feng, Assistant Research Scientist – The University of Michigan Transportation Research Institute
Henry Liu, Director – Center for Connected and Automated Transportation (CCAT)
Director – Mcity
Professor of Civil and Environmental Engineering – The University of Michigan
Research Professor – The University of Michigan Transportation Research Institute

Project Abstract:
This project aims at developing new science and technology of vehicle trajectory based traffic control, especially under lower penetration of CAVs. Most of the existing models require at least a moderate penetration rate (e.g., 30%) to be effective. How to estimate real-time traffic condition and perform control under lower penetration rate (e.g., <10%) is still an open question. In addition, when the vehicle control is incorporated into signal control, usually a fully CAV environment is assumed. The interactions between CAVs and regular vehicles in a mixed traffic condition are not thoroughly investigated.

Institution(s): University of Michigan Transportation Research Institute

Award Year: 2018

Research Thrust(s): Control & Operations

Project Form(s):

Project Information Form
Final Report