Serverless Cloud-based Speed Advisory Application for Connected Vehicles – Case Study

Abstract In this study, we develop a real-time connected vehicle (CV) speed advisory application, which we refer to as “Serverless CloSA”, using commercial cloud services and present case studies for a signalized corridor for different roadway traffic conditions. First, we develop a highly scalable serverless cloud computing architecture using Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we develop an optimization-based real-time CV speed advisory algorithm that is deployable in the cloud. Third, we develop a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Then, we conduct three case studies for three different roadway traffic conditions, i.e., low, medium, and high-density traffic. Our analyses show that Serverless CloSA can reduce the average stopped delays at signalized intersections in a corridor by 77% while reducing the aggregated risk of collision by 21% compared to the baseline scenario, i.e., no speed advisory for the CVs. Our experiments show an average end-to-end delay of 452 ms, which is well under the 1000 ms delay threshold of real-time CV mobility applications. Thus, this study also demonstrates the feasibility of deploying a real-time CV mobility application using commercial cloud services.