Date of Award
Master of Science (MS)
School of Computing
The objective of this study is to assess the feasibility of cloud-based real-time connected vehicle (CV) applications. The author developed a cloud-based speed advisory application for CVs in a signalized corridor (COSACC) to achieve this objective. The contribution of this study is threefold. First, it introduced a serverless cloud computing architecture using Amazon Web Services (AWS) for real-time CV applications. Second, the author developed a real-time optimization-based speed advisory algorithm that is deployable in AWS. Third, this study utilized a cloud-in-the-loop simulation testbed using AWS and Simulation of Urban Mobility (SUMO), which is a microscopic traffic simulator. The author conducted experiments on cloud access at three-hour intervals over 24 hours in one day. These experiments revealed that the total data upload and download time to and from AWS via LTE is on average 92 milliseconds, which meets the allowable delay requirement for real-time CV traffic mobility applications. The author conducted a case study by implementing the COSACC in a cloud-in-the-loop simulation testbed. The analyses revealed that COSACC can reduce vehicle stopped delay at the signalized intersections up to 98% and fuel consumption in the signalized corridor up to 12.7%, compared to the baseline scenario, i.e., no speed advisory on the signalized corridor. Moreover, the authors observed an average end-to-end delay from a CV sending basic safety messages to it receiving a speed advisory from the cloud to be about 443 ms, which is well under the 1000 ms threshold required for any real-time traffic mobility application for connected vehicles.
Deng, Hsien-Wen, "COSACC: Cloud-Based Speed Advisory for Connected Vehicles in a Signalized Corridor" (2020). All Theses. 3466.