Design and Implementation of a Novel Architecture Based on Digital Twin and GIS for Intelligent Traffic Monitoring

Subject Areas :

zahra rezaee ¹ , hossein aghamohammadi ² , محمد حسن وحیدنیا ³ , زهرا عزیزی ⁴ , Saeed Behzadi ⁵

1 -
2 - Assistant Professor, Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - -3Assistant Professor, Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
4 - Assistant Professor, Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
5 - Associated Professor, Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

Received: 2025-03-13 Accepted : 2025-06-13 Published : 2025-07-24

Keywords: Smart City, Digital Twin, Urban Traffic Management, GIS, Traffic Prediction,

Abstract :

The rapid growth of urbanization and the increasing number of motor vehicles have created significant challenges in urban traffic management. Heavy traffic not only leads to time wastage and increased air pollution but can also hinder quick and effective responses to emergencies such as accidents and natural disasters. In this context, advanced technologies such as Digital Twin and Geographic Information Systems (GIS) have gained attention as powerful tools for intelligent traffic management and improving emergency responses. This research aims to develop a location-based simulation model using Digital Twin and GIS to predict traffic and enhance urban traffic management. In this study, a digital twin system was designed and implemented using real-time data, such as traffic information and weather conditions, leveraging online services. The results demonstrate that this system can accurately monitor and predict traffic conditions, and its capabilities can subsequently be utilized in applications such as optimal routing and traffic reduction. Additionally, the online dashboard developed in this research provides real-time access to traffic and environmental data, significantly aiding decision-making and urban traffic management. This innovative approach represents a significant step toward smart traffic management and lays the groundwork for creating smart cities and improving urban quality of life.

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