IOT based Smart solution for Traffic congestion at Metro Cities

Abstract

The major challenge in metro cities is to handle the vehicle traffic as well as pedestrian traffic at major locations. Over a day nearly 20% time is spend in our work or personal time to over come the delay at traffic to reach the destination. Traffic management systems have been the subject of much study, but because of the blending of technologies like the Internet of Things (IoT) and artificial intelligence, research on intelligent traffic monitoring is currently ongoing (AI). By integrating these technologies, we can improve decision-making processes and promote urban development. The management of urban and highway traffic is the primary emphasis of the available traffic prediction techniques, and there are few studies that have concentrated on collector roads and closed campuses. Furthermore, when the consumers are not using any smart gadgets, it is difficult to reach out to the public and create active relationships to help them make decisions. In order to gather, analyse, and store real-time traffic data for such a situation, this study suggests an IoT-based system paradigm. The goal is to increase mobility by using roadside messaging devices to provide real-time traffic reports on traffic congestion and unexpected traffic events. These time-saving alerts will benefit residents, particularly during rush hour. Also disseminated by the system are the administrative authorities' traffic updates. The results of the trials demonstrate high accuracy in vehicle recognition and a low relative error in road occupancy prediction. A prototype is used to assess the viability of the model. The research project, which is financed by Omani, is looking at Real-Time Feedback for Adaptive Traffic Signals.