ERI: Advancement of Max Pressure Traffic Signal Control in Partially Connected and Automated Transportation Systems

This Engineering Research Initiation (ERI) project will support research that attempts to advance traffic network efficiency in the transitional era of mixed human driven vehicles (HDV) and connected and autonomous vehicles (CAV). The research looks to redefine Max Pressure (MP) based traffic control systems to address unique challenges presented by this transition and to leverage advanced capabilities. MP is a scalable and effective decentralized traffic signal control strategy for large scale networks, thanks to its simple control mechanism and guarantee of maximum throughput. However, certain fundamental and practical limitations hinder its implementation. Research funded by this ERI project seeks to address these MP limitations to pave the way for their implementation in mixed HDV-CAV environments, which are expected to dominate urban transportation in the coming decades. The findings from this project are anticipated to promote the progress of transportation science and advance national prosperity by fostering innovative solutions to enhancing traffic operational efficiencies during this transitional era. This award will support the initiation of a sustainable research group and provide valuable training opportunities for the next generation of transportation engineers at Jackson State University.

The first goal of this research project is to improve the theoretical foundation of existing MP algorithms by analyzing and comparing various MP control structures under diverse traffic conditions. The project will also leverage the theory of the Macroscopic Fundamental Diagram (MFD) to attempt to extend traditional intersection-level MP control to regional-level MP control, incorporating time-varying perimeter locations. These advancements will look to provide guidance for learning-based traffic management in complex traffic conditions. The second goal is to incorporate advanced CAV technology into MP architecture. This involves developing theoretical algorithms for CAV routing and trajectory planning that cooperate with MP control logic to achieve multi-objective optimization and enhance regional traffic efficiency. By focusing on mixed HDV-CAV environments, these developments look to address the transitional challenges of urban transportation systems, boosting the implementation of MP algorithms and improving operational efficiency at the regional level.

This project is jointly funded by Civil Infrastructure Systems (CIS) program and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2554235
Principal Investigator: Hao Liu
Funds Obligated: $199,755
State: ME