Optimum Connected Vehicle Speed Control on Signalized Roadways in Mixed Flow

Project Abstract

This research aims to develop an optimal speed control strategy for connected vehicles (CVs) considering mixed engine types including conventional internal combustion vehicles (ICEVs), battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs), in the vicinity of signalized intersections. According to the findings of previous studies, the optimal speed trajectories for vehicles with different engine types are very different under certain conditions, which may create different speeds of traffic resulting in shockwaves increasing the crash risk. Therefore, the team will use multi-agent modeling to develop a general speed control strategy, which calculates a compromised solution across different engine types but optimizes the mixed traffic flow in the entire network. Thereafter, the team will use the control strategy to develop CV automated and manual control algorithms. The automated controller will be implemented and tested in a microscopic traffic simulation software to quantify the system-wide impacts on traffic mobility, vehicle energy consumption and emissions for different combinations of vehicle types and various traffic conditions. In addition, the proposed algorithm for mixed traffic conditions will be compared with the algorithms previously developed by the team for each individual vehicle type so that we can investigate the performances of system-optimized control versus individual-optimized control in simulated traffic networks. Lastly, the manual mode controller will be tested by participants using driving simulators at MSU by following simple driving instructions (such as speed up, down, maintain, either using visual color or audio alerts). It is anticipated that the proposed controllers will improve the mobility of arterial traffic by reducing delays, energy consumption and vehicle emissions.

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Universities Involved

Virginia Tech

Morgan State University

Principle Investigators

Dr. Hao Chen, Virginia Tech

Dr. Hesham Rakha, Virginia Tech

Dr. Mansoureh Jeihani, Morgan State University

Dr. Eazaz Sadeghvaziri, Morgan State University

Funding Sources and Amounts

USDOT: $120,000; Virginia Tech: $40,000 (match); Morgan State $20,000 (match)

Start Date

Oct. 1, 2021

Completion Date

May 1, 2023

Expected Research Outcomes

The proposed research effort will be the first study to develop a general eco-driving strategy for vehicles with mixed engine types at signalized intersections. According to the findings of previous studies, the optimal speed trajectories for vehicles with different engine types are very different under certain conditions, which may create different speeds of traffic resulting in shockwaves increasing the crash risk. This research will develop an optimal speed control strategy for connected vehicles (CVs) considering mixed engine types including conventional internal combustion vehicles (ICEVs), battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs), in the vicinity of signalized intersections. It is anticipated that the proposed controllers will improve the mobility of arterial traffic by reducing delays, energy consumption and vehicle emissions.

Expected Equity Impacts and Benefits of Implementation

The proposed speed control system will be the first eco-driving system that can be used for CAVs and CVs under mixed traffic conditions including ICEVs, BEVs and HEVs. Considering that currently most vehicles on the road do not have automated control, the proposed manual mode controller ensures that human drivers can follow simple driving instructions to pass signalized intersections with less energy consumption and delay. The automated mode controller can help CAVs to achieve even more savings by following energy-optimized trajectories more precisely, compared to human drivers. In addition, the tests in microscopic simulation software and driving simulator will be beneficial for government stakeholders and industry companies to estimate the benefits of implementing the proposed system.

Subject Areas

Connected and Automated Vehicle, Vehicle Speed Control, Traffic Signal Control, Mixed Traffic Flow, Microscopic Traffic Simulation, Driving Simulator