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Development of a Cross-Platform Architecture for the Analysis of Vehicular Traffic in a Smart City with Machine Learning Tools

Abstract:

The use of the Internet today has grown by leaps and bounds both in mobile phones, appliances, televisions, computers, so the link between objects and people is more daily. Services such as cloud computing and the IoT Internet of things have had a significant advance along with machine learning for managing pbkp_redictions. For this reason, this article presents a cross-platform architecture for the analysis of vehicular traffic in a smart city with machine learning tools based on model engineering to generate pbkp_rediction tools. For the architecture design, MDA Model-Driven Architecture techniques were used, and services were implemented in AWS Amazon Web Service. To validate the proposal, the usability of the interface was analyzed, and load tests were applied to the services.