Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor

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 publ...

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Main Authors: Hsien-Wen Deng, M Sabbir Salek, Mizanur Rahman, Mashrur Chowdhury, Mitch Shue, Amy W. Apon
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-03-01
Series:International Journal of Transportation Science and Technology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2046043024000352
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author Hsien-Wen Deng
M Sabbir Salek
Mizanur Rahman
Mashrur Chowdhury
Mitch Shue
Amy W. Apon
author_facet Hsien-Wen Deng
M Sabbir Salek
Mizanur Rahman
Mashrur Chowdhury
Mitch Shue
Amy W. Apon
author_sort Hsien-Wen Deng
collection DOAJ
description 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 the signalized intersections 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.
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publishDate 2025-03-01
publisher KeAi Communications Co., Ltd.
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spelling doaj-art-15646aba2af44a83a0f9a242ff2342eb2025-08-20T03:17:51ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302025-03-011713114710.1016/j.ijtst.2024.03.004Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridorHsien-Wen Deng0M Sabbir Salek1Mizanur Rahman2Mashrur Chowdhury3Mitch Shue4Amy W. Apon5School of Computing, Clemson University, Clemson, SC 29631, USAGlenn Department of Civil Engineering, Clemson University, Clemson, SC 29631, USA; Corresponding author.Department of Civil, Construction & Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USAGlenn Department of Civil Engineering, Clemson University, Clemson, SC 29631, USASchool of Computing, Clemson University, Clemson, SC 29631, USASchool of Computing, Clemson University, Clemson, SC 29631, USAIn 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 the signalized intersections 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.http://www.sciencedirect.com/science/article/pii/S2046043024000352Public cloudCyber-physical systemConnected vehicle (CV)Roadway traffic managementAmazon Web Services (AWS)
spellingShingle Hsien-Wen Deng
M Sabbir Salek
Mizanur Rahman
Mashrur Chowdhury
Mitch Shue
Amy W. Apon
Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
International Journal of Transportation Science and Technology
Public cloud
Cyber-physical system
Connected vehicle (CV)
Roadway traffic management
Amazon Web Services (AWS)
title Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
title_full Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
title_fullStr Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
title_full_unstemmed Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
title_short Leveraging public cloud infrastructure for real-time connected vehicle speed advisory at a signalized corridor
title_sort leveraging public cloud infrastructure for real time connected vehicle speed advisory at a signalized corridor
topic Public cloud
Cyber-physical system
Connected vehicle (CV)
Roadway traffic management
Amazon Web Services (AWS)
url http://www.sciencedirect.com/science/article/pii/S2046043024000352
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