Smart City Traffic Optimization using IoD and IoT Integration

Urban traffic congestion, along with the resulting fuel waste (largely due to travel delays) and pollution, poses increasing challenges as city populations grow. There is a critical need for techniques to mitigate these effects while ensuring traffic efficiency. In this research, we propose a system that integrates Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to improve urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely traffic updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination over larger areas, particularly those with sparse connectivity. To optimize drone placement for maximum coverage, the Particle Swarm Optimization (PSO) algorithm was employed. Using the SUMO simulator, we conducted experiments in two urban scenarios: Dammam (Saudi Arabia) and Doha (Qatar). Python and the Traffic Control Interface (TraCI) were used to implement functionalities such as the PSO, communication protocols, and dynamic rerouting. Results show that the system reduces vehicular emissions by up to 41\% and travel times by up to 32\% in Dammam, and up to 48\% and 44\% respectively in Doha, outperforming traditional Vehicle-to-Vehicle (V2V) systems used as a baseline. These outcomes demonstrate that integrating IoT and drones can minimize travel delays, reduce emissions, improve traffic flow, and ultimately enhance air quality while supporting sustainability in smart cities.