HYBRID CLOUD-BASED INTELLIGENT TRAFFIC MONITORING IOT SYSTEM FOR A RESIDENTIAL AREA

Abstract

B a c k g r o u n d . This paper presents a new architecture for an intelligent transportation system (ITS) that leverages Internet of Things (IoT) technologies and the Azure cloud platform. The scientific novelty lies in the development of an architecture that integrates edge computing, cloud services, and machine learning algorithms for adaptive traffic management based on real-time data. The proposed architecture efficiently processes traffic flow information, performs modeling, and automatically adjusts traffic signals to reduce congestion. The effectiveness of the architecture has been validated through a series of experiments focused on vehicle recognition, traffic signal optimization, and real-time monitoring of the traffic situation. M e t h o d s . The methods used include computer simulation modeling for managing the intelligent transportation system, reinforcement learning for training the system, and computer vision techniques for vehicle recognition. R e s u l t s . The proposed ITS architecture is based on IoT technologies, enabling real-time data collection and analysis of road traffic. The developed system was tested in various urban areas with different levels of traffic load. The experiments demonstrated that t he system can adaptively adjust traffic signals based on traffic analysis, significantly improving road capacity and reducing congestion. C o n c l u s i o n s . The results of the experiments confirmed the effectiveness of the proposed intelligent transportation system architecture. Future research may focus on enhancing the system by incorporating more advanced artificial intelligence algorithms for automating traffic signal management decisions.