Smart city traffic optimization using IoD and IoT integration

Area of Science:

• Intelligent Transportation Systems
• Wireless Communication Networks
• Environmental Science

Background:

• Urban traffic congestion leads to significant fuel waste and pollution.
• Growing city populations exacerbate traffic issues, necessitating efficient management solutions.
• Existing traffic management systems struggle with real-time data dissemination and coverage in all areas.

Purpose of the Study:

• To propose and evaluate an integrated framework using Internet of Things (IoT) and Internet of Drones (IoD) for enhanced urban traffic management.
• To improve traffic efficiency, reduce travel delays, and minimize vehicular emissions in urban environments.
• To leverage drone technology for dynamic communication coverage extension in areas with sparse connectivity.

Main Methods:

• Utilized Internet of Things (IoT) sensors for real-time traffic monitoring.
• Employed Roadside Units (RSUs) for data processing and vehicle updates.
• Implemented drones as mobile relay nodes to extend communication range, optimizing placement with Particle Swarm Optimization (PSO).
• Conducted simulations using SUMO and TraCI with Python for communication protocols and adaptive rerouting.

Main Results:

• The integrated IoT-IoD system demonstrated substantial reductions in travel delays and vehicular emissions.
• In Dammam, emissions decreased by up to 40.99% and travel times by 32.05%.
• In Doha, emissions decreased by up to 48.78% and travel times by 43.92%.

Conclusions:

• Coordinated IoT-IoD systems effectively enhance urban traffic flow and operational efficiency.
• The proposed framework significantly contributes to sustainable urban mobility and improved air quality.
• Dynamic drone integration offers a scalable solution for real-time traffic information dissemination.