Using Internet of Things and Artificial Intelligence Applications to Design a Smart Traffic System

Traffic congestion in cities is an ongoing problem caused by urbanization, excessive vehicle density and infrastructure. The presence of conventional traffic control signal systems that makes use of fixed signal timings is not adequate enough when it comes to handling dynamic traffic flows on real time basis. Currently, the proposed approach in the context of this paper implies integration of Internet of Things (IoT) and Artificial Intelligence (AI) in order to create a smart traffic management system that would be suitable in the context of a smart city. System architecture allows capturing real-time data, including vehicle flow, speed, and weather conditions using IoT-connected sensors and monitoring gadgets. This information is processed by AI models, i.e., Convolutional Neural Networks (CNN) in vehicle detection, Long Short-Term Memory (LSTM) in time series forecasting, and Deep Q-Networks (DQN) in adaptive signal control. The architecture involves the use of hybrid edge cloud computing models in the provisioning of low latency coupled with scalable analytics. Simulations of empirical analyses conducted on the simulated traffic conditions indicate that congestion level drops by up to 48%, and the average vehicle waiting time reduces by 35%. The system also assists in fuel savings as well as emissions minimized by diminishing stop-and-go traffic trends. The findings confirm the opportunities that IoT-AI integration will have on enhancing the mobility of cities and making the traffic effective and allowing the current smart cities to make decisions based on data.