Communication Protocols and Data Transmission in Machine Learning and IoT-based Traffic Management System

As urbanization picks up speed, effectively managing traffic is a significant obstacle to the creation of smart cities. In order to address this issue, a novel strategy is proposed in this research that combines machine learning (ML) with Internet of Things (IoT) technologies. Designing and putting into place a flexible traffic-management system that maximizes traffic flow, reduces congestion, and improves overall transportation efficiency is the main goal. The suggested solution makes use of real-time data gathered from various Internet of Things (IoT) devices, including congestion sensors and cameras, and GPS-enabled automobiles. The ML algorithms can continuously examine and gain insight into patterns, trends, and irregularities in traffic patterns thanks to these data sources' comprehensive views of the traffic conditions. These data are used by the machine learning (ML) algorithms used in the system to forecast possible traffic events, identify crowded regions, and predict traffic patterns. These forecasts give the traffic- management system the ability to dynamically change the timing of traffic signals, redirect cars, and distribute resources in response to the shifting traffic conditions. The article also explores the structure and communication protocols required to link the IoT devices with the machine learning (ML) traffic-management system in order to achieve seamless integration. In order to ensure the system's durability and dependability in real-world circumstances, it also handles the issues related to confidentiality of information, security, and scalability.