Leveraging Connected and Automated Vehicles for Participatory Traffic Control

Abstract

This report lays the theoretical groundwork for participatory traffic control that integrates traditional infrastructure like traffic signals with connected and automated vehicles (CAVs) acting as mobile actuators. The study is divided into two main parts. First, we introduce a robust traffic state estimation method that leverages real-time data from CAVs to yield precise insights into the transportation system. Second, we present an analytical framework to guide the strategic control of CAVs, aiming to indirectly influence the behavior of human-driven vehicles and optimize traffic flow across various time periods and transportation facilities. Our research serves as a foundational step towards the practical deployment of participatory traffic control systems, contributing to the creation of smarter, more efficient transportation networks for the future.