Collaborative Research: Mixed Traffic Dynamics Under Disturbances: Impact of Multi-Class Connected and Automated Vehicles

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Connected and Automated Vehicle (CAV) technologies have garnered huge interest across private industry, academia, government, and the public. A wide range of benefits are predicted when these ground-breaking technologies become mature, including higher road efficiency, improved safety, and better energy consumption and emissions. However, these benefits will be open to question until the technologies sufficiently mature. Specifically, a major uncertainty in benefits lies in mixed traffic of CAVs and human-driven vehicles (HDVs), where interactions between them remain largely unknown. Therefore, in the foreseeable future, traffic will likely be mixed with multiple classes of CAVs and HDVs. This project will aim to better understand the anticipated behavior of this mixed traffic system, and its impact on traffic in order to help fully utilize the potentials of the CAV technology. The results will guide the development of traffic management strategies, policies, and long-term planning for the future transportation system. This project will also engage in a range of integrated research, educational and outreach activities that will extend the knowledge obtained from this research to a broader audience, including developing simulation-based educational modules, organizing workshops, sharing simulation platform for mixed traffic, and engaging undergraduate and graduate students, particularly underrepresented groups, in the research and education. This research aims to understand how HDVs and different classes of CAVs will interact under traffic disturbances that cause (momentary) reductions in speed and affect traffic flow performance. Specifically, this project will aim to (1) characterize discernable differences in the car-following behavior of HDVs and CAVs of different classes under disturbances; and (2) elucidate their effects on traffic flow throughput and traffic flow stability. To this end, this research will develop a systematic method to bring together different control modeling paradigms for CAVs into a unifying framework to unveil their individual and collective impacts on traffic flow throughput and stability. Three CAV control paradigms will be considered in this study: linear control, model predictive control (MPC), and artificial-intelligence-based control. The vehicle-level investigation of complex interactions among CAVs and HDVs will unveil the interaction mechanisms and elucidate how they scale up to the collective behavior of traffic stream, which will inspire new modeling paradigms to describe mixed traffic flow dynamics and control CAVs. 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.