摘要: |
It can be expected that connected vehicles (CVs) systems will soon go beyond testbed and appear in real-world applications. To accommodate the large number of connected vehicles on the roads, arterial signal control systems would require supports of various components such as roadside infrastructures, vehicle on-board devices, effective communication network, and optimal control algorithms. In this project, the research team aims to establish a real-time and adaptive system for supporting the operations of CV-based signalized arterial. One unique feature of this project is the team would classify CVs into three types, i.e., Type-I CVs, Type-II CVs, and connected automated vehicles (CAVs). Then the proposed system will prioritize the communication needs of different types of CVs and best utilize the capacity of the communication channels. The CV data sensing and acquisition protocol, built on a newly developed concept of Age of Information (AoI), will support the feedback control loop to adjust signal timing plans. The multidisciplinary research team, including researchers from transportation engineering and electrical engineering, will carry out the project tasks along four directions that capitalized on the PIs� expertise: (1) Data collection and communication, in which the proposed system will be based on the AoI, prioritize the data needs of different types of CVs, and optimize the communication network; (2) Traffic Signal Control, which will concurrently optimize real-time traffic signal plans and vehicle trajectories; (3) Multimodal system design, which will integrate Transit Signal Priority (TSP) for accommodating connected buses; (4) System evaluation, where the research team will work with Utah Department of Transportation (UDOT) for testing the proposed system under their current implementation framework on Redwood Rd., Salt Lake City. This project will address the urgent needs in CV system designs and offer control foundations to support the operations of urban signalized arterial under CV environment. |