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New Innovation and Idea | Computer Engineering | India | Volume 13 Issue 10, October 2024 | Popularity: 5.5 / 10

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Using Support Vector Machines (SVM) for Intelligent Traffic Management in Microservices Environments

Anuradha Walia, Mayur Prakashrao Gore

Abstract: This study examines the use of the Support Vector Machines (SVM) in smart traffic management in microservices contexts. Due to growing problem of congestions and arising safety issues in connection with proceeding urbanization, conventional means to control traffic no longer suffice the needs of the modern world. In this work, we discuss how to incorporate SVMs into a systems architecture comprising many loosely-coupled microservices, so that traffic can be managed based on the processing of data in real-time. Data gathering is done from several sources; building of models is done as well as applied in the microservices architecture. Mean Accuracy; Mean Precision; Mean Recal/ Mean F1-score; Average travel time; Mean percentage of congestion reduction establishes the efficiency of the proposed SVM model. The analysis shows a notable increase in traffic efficiency and safety: the average travel time has been decreased by 28%, the congestion level - by 35%. The results of this work show that SVMs can be used in improving the efficacy of traffic managing systems that characterise the modern city, thus offering an avenue for higher efficiency in managing the traffic patterns that define the urban context.

Keywords: Support Vector Machines, intelligent traffic management, microservices architecture, congestion prediction, real-time data processing

Edition: Volume 13 Issue 10, October 2024

Pages: 154 - 161

DOI: https://www.doi.org/10.21275/SR24928124030

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