In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based real-time CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at signalized intersection of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.

翻译：在本研究中，我们开发了一款利用公共云服务的实时网联车辆（CV）速度建议应用，并在模拟信号化走廊中针对不同道路交通条件进行了测试。首先，我们利用亚马逊云服务（AWS）提供的公共云服务构建了一种可扩展的无服务器云计算架构，以支持实时CV应用的需求。其次，我们采用模块化设计方法开发了基于优化的实时CV速度建议算法，通过无服务器架构使应用能够自动扩展并部署在云端。第三，我们利用AWS和开源微观道路交通模拟器SUMO（模拟城市交通）构建了云端在环仿真测试平台。基于不同道路交通条件的分析表明，该无服务器CV速度建议应用满足了实时CV出行应用的延迟要求。此外，我们的无服务器CV速度建议应用将信号化走廊交叉口的平均停车延误降低了77%，并将综合碰撞风险降低了21%。这些结果证明了利用公共云基础设施在CV环境下实施实时道路交通管理应用的可行性与有效性。