CAREER: Tackling Congestion in Smart Cities via Data-Driven Optimization-Based Control of Connected and Automated Vehicles

Michigan Technological University, Houghton MI

Investigators

Abstract

This Faculty Early Career Development Program (CAREER) award supports fundamental research needed to use real-time high-frequency data from many connected vehicles to automatically control driving and routing decisions. This research is interdisciplinary in nature as it requires the understanding of research and education approaches in various fields including transportation systems, operations research, control theory, vehicle dynamics, data science, and computer science. The project will contribute to training the next generation of scientists and engineers in multidisciplinary perspectives through curriculum development and research involvement. The PI will work closely with high school teachers and students, targeting underrepresented minorities, through a Summer Internship Program for first-generation college students and a Youth Development Mentoring Program using a set of online tools and case studies. The project is to provide real-time, online, and predictive robust optimal driving and routing decisions to connected and automated vehicles. Ultimately, the project will help address traffic congestion in smart cities. This research will fill knowledge gaps in the integration of real-time high-frequency connected vehicle data into online robust automated driving and routing control systems. Specifically, this research will (1) suggest data-driven optimization-based model predictive control (MPC) models through the concepts of "forecasting the forecasts of others" and online learning for cooperative connected and automated driving under traffic uncertainty; (2) integrate n-person dynamic routing games and MPC models for non-cooperative connected and automated routing under player and capacity uncertainties; and (3) balance cooperative automated driving and non-cooperative automated routing through optimizing platoon formation to increase network efficiency. To address computational scalability issues in large scale networks, this research will also explore fictitious play type algorithms to solve the n-person dynamic routing games, as well as leverage distributed optimization to address computational issues in platoon formation problems. Simulation analysis and road testing will be conducted for feasibility analysis of the models using a fleet of connected and automated vehicles. 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.