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Iot-enabled Adaptive Traffic Management: A Multiagent Framework For Urban Mobility Optimisation.

Home
Iot-enabled Adaptive Traffic Management: A Multiagent Framework For Urban Mobility Optimisation.

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IoT-Enabled Adaptive Traffic Management: A Multiagent Framework for Urban Mobility Optimisation.

Ibrahim Mutambik¹

¹Department of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 12, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Internet of Things (IoT)-enabled adaptive traffic management significantly reduces urban congestion and emissions. This sensor-driven approach enhances mobility and offers a sustainable solution for smart city planning.

Keywords:

IoT sensor smart cities sustainability traffic management urban urban mobility optimisation

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Area of Science:

Urban Planning & Transportation Engineering
Computer Science & Artificial Intelligence
Environmental Science

Background:

Urban congestion and environmental impact are major challenges in densely populated cities.
Conventional traffic management systems often rely on static or reactive strategies, proving insufficient for dynamic urban environments.

Purpose of the Study:

To evaluate the effectiveness of IoT-enabled adaptive traffic management systems in mitigating urban congestion and environmental impact.
To develop and assess advanced traffic management strategies using a multiagent simulation framework.

Main Methods:

Developed a multiagent simulation framework integrating real-time IoT sensor data with predictive analytics.
Modeled diverse traffic actors (vehicles, buses, cyclists, emergency services) as dynamic agents.

Assessed adaptive signal control and dynamic rerouting strategies under various traffic scenarios.

Main Results:

Achieved a 30% reduction in average travel times.
Reduced congestion at major intersections by 50%.
Decreased carbon dioxide (CO2) emissions by 28%.

Conclusions:

IoT-enabled adaptive traffic management systems offer transformative potential for sustainable urban mobility.
The proposed framework provides a realistic and scalable evaluation of smart city transportation strategies.
Findings offer actionable insights for urban planning and transportation policy, emphasizing scalability, equity, and multimodal inclusivity.