طراحی و پیادهسازی یک معماری نوین مبتنی بر همسان رقومی و GIS در پایش هوشمند ترافیک
محورهای موضوعی : کاربرد GIS&RS در برنامه ریزی شهری
زهرا رضایی
1
,
حسین آقامحمدی
2
,
محمد حسن وحیدنیا
3
,
زهرا عزیزی
4
,
سعید بهزادی
5
1 - دانشجوی دکتری RS-GIS، گروه سنجش از دور و سیستم اطلاعات مکانی، دانشگاه آزاد اسلامی ایران، واحد علوم و تحقیقات تهران
2 - استادیار گروه سنجش از دور و GIS، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران
3 - استادیار مرکز مطالعات سنجش از دور و GIS، دانشکده علوم زمین دانشگاه شهید بهشتی، تهران، ایران
4 - استادیار گروه سنجش از دور و GIS، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران
5 - استادیار گروه مهندسی نقشه برداری، دانشکده مهندسی عمران، آب و محیط زیست دانشگاه شهید بهشتی، تهران، ایران
کلید واژه: شهر هوشمند, همسان رقومی, مدیریت ترافیک شهری, سیستمهای اطلاعات جغرافیایی, پیشبینی ترافیک,
چکیده مقاله :
رشد سریع شهرنشینی و افزایش تعداد وسایل نقلیه موتوری، چالشهای جدی در مدیریت ترافیک شهری ایجاد کرده است. ترافیک سنگین نهتنها منجر به اتلاف زمان و افزایش آلودگی هوا میشود، بلکه در شرایط اضطراری مانند حوادث و بلایای طبیعی، میتواند مانع از واکنش سریع و مؤثر در محلهای حادثه شود. در این راستا، فناوریهای نوین مانند همسان رقومی (DT[1]) و سیستمهای اطلاعات جغرافیایی (GIS [2]) به عنوان ابزارهای قدرتمند در مدیریت هوشمند ترافیک و بهبود پاسخهای اضطراری مورد توجه قرار گرفتهاند. این تحقیق با هدف توسعه یک مدل شبیهسازی مکانمحور مبتنی بر همسان رقومی و GIS برای پیشبینی ترافیک و بهبود مدیریت ترافیک شهری انجام شده است. در این مطالعه، از دادههای زمان واقعی مانند اطلاعات ترافیکی و شرایط آبوهوایی، با بهرهگیری از سرویسهای برخط، یک سیستم همسان رقومی طراحی و پیادهسازی شده است. نتایج نشان میدهد که این سیستم قادر است با دقت بالایی شرایط ترافیکی را پایش و پیشبینی کند و متعاقبا نیز از قابلیتهای آن در کاربردهایی مانند مسیریابی بهینه و کاهش ترافیک میتوان استفاده نمود. همچنین، داشبورد آنلاین توسعهیافته در این تحقیق، امکان دسترسی بلادرنگ به اطلاعات ترافیکی و محیطی را فراهم میکند، که به بهبود تصمیمگیری و مدیریت ترافیک شهری کمک شایانی مینماید. این رویکرد نوآورانه، گامی مهم در جهت هوشمندسازی مدیریت ترافیک و زمینهساز ایجاد شهرهای هوشمند و بهبود کیفیت زندگی شهری است.
[1] Digital Twin
[2] Geospatial Information System
Introduction
Rapid urbanization and the accompanying growth in the number of motor vehicles have created complex challenges in urban traffic management. Traditional traffic monitoring and control systems often fail to deliver real-time adaptability, predictive insight, and integrated spatial awareness. These limitations result in inefficient traffic flow, increased emissions, and slower responses to emergencies such as accidents or natural disasters. In recent years, the integration of Digital Twin (DT) technologies with Geographic Information Systems (GIS) has emerged as a promising solution for developing intelligent, data-driven traffic management systems. Digital Twins enable the creation of virtual replicas of physical entities, enabling continuous monitoring, simulation, and optimization based on real-time data. By combining DT with GIS, it becomes possible to simulate dynamic traffic environments in a spatially accurate context, analyze system performance, and assist in decision‑making for sustainable and resilient urban transportation systems.
Materials and Methods
The study employs a Digital Twin–based architecture integrated with GIS tools to design and implement a smart traffic monitoring and prediction system. The system collects and processes real-time data from online traffic monitoring services, weather APIs, and environmental sensors. These data streams are continuously synchronized with the digital twin model, which dynamically mirrors real-world traffic conditions. Spatial and temporal analysis is performed using GIS to link traffic behaviors with geographical and environmental factors. Advanced data analytics and predictive algorithms are applied to forecast congestion levels, detect anomalies, and propose optimal routes. The system architecture includes:
- Data acquisition and integration layer (real-time traffic, weather, and environmental data),
- Digital Twin core module (simulation and synchronization engine),
- GIS analysis module (spatial data visualization and mapping), and
- Decision-support dashboard for real-time visualization and policy formulation.
Results and Discussion
The implemented digital twin model successfully demonstrated its capability to replicate and visualize real-world traffic conditions in real time. By integrating weather and environmental data, the model provided enhanced situational awareness and more accurate traffic prediction compared to traditional methods. Simulation outcomes indicated that the proposed system could identify congestion patterns, predict potential bottlenecks web and suggest alternative routing strategies to reduce travel time and emissions. Additionally, the web-based dashboard allowed stakeholders to interact with live traffic and environmental data through a spatially rich interface. The predictive analytics improved response efficiency to traffic incidents and facilitated proactive urban traffic control. The system’s modular architecture ensures scalability, enabling future integration with additional smart city data sources such as public transport systems and emergency services.
Conclusion
This research introduces an innovative Digital Twin–GIS framework that enhances the intelligence, adaptability, and responsiveness of urban traffic management systems. The integration of real-time data, spatial analytics, and predictive modeling enables city managers to make informed, data-driven decisions. The developed architecture demonstrated its potential for optimizing traffic flow, reducing congestion, and improving environmental sustainability. Moreover, it lays a strong foundation for the broader development of smart city ecosystems, contributing significantly to improved mobility, safety, and urban quality of life. Future research may expand the system to include autonomous vehicle data, crowd mobility analytics, and IoT‑based sensor networks for fully integrated urban transportation management.
1) Manfred Boltze, Vu Anh Tuan, Approaches to Achieve Sustainability in Traffic Management, Procedia Engineering, Volume 142, 2016, Pages 205-212, ISSN 1877-7058, https://doi.org/10.1016/j.proeng.2016.02.033.
2) R. Abdellah, O. A. K. Mahmood, A. Paramonov and A. Koucheryavy, "IoT traffic prediction using multi-step ahead prediction with neural network," 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2019, pp. 1-4, doi: 10.1109/ICUMT48472.2019.8970675.
3) Nie L, Wang X, Zhao Q, Shang Z, Feng L, Li G. DT for transportation big data: a reinforcement learning-based network traffic prediction approach. IEEE Transactions on Intelligent Transportation Systems. 2023 Jan 18;25(1):896-906.
4) Zhang Y, Zhang H. Urban digital twins: decision-making models for transportation network simulation. In Proceedings of the 2022 International Conference on Computational Infrastructure and Urban Planning 2022 Jun 18 (pp. 18-21).
5) Rezaei Z, Vahidnia MH, Aghamohammadi H, Azizi Z, Behzadi S. Digital twins and 3D information modeling in a smart city for traffic controlling: A review. Journal of Geography and Cartography. 2023 Jun 27;6(1):1865.
6) Muthuramalingam S, Bharathi A, Rakesh Kumar S, Gayathri N, Sathiyaraj R, Balamurugan B. IoT based intelligent transportation system (IoT-ITS) for global perspective: A case study. Internet of things and big data analytics for smart generation. 2019:279-300.
7) Vaidya RB, Kulkarni S, Didore V. Intelligent transportation system using IOT: A Review. Int. J. Res. Trends Innov. 2021;6:80-7.
8) Schleich B, Anwer N, Mathieu L, Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals 2017; 66(1): 141–144. doi: 10.1016/j.cirp.2017.04.040
9) Korth B, Schwede C, Zajac M. Simulation-ready digital twin for realtime management of logistics systems. In2018 IEEE international conference on big data (big data) 2018 Dec 10 (pp. 4194-4201). IEEE.
10) Grieves M, Vickers J, Twin D. Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems. 2016:85-113.
11) Liu M, Fang S, Dong H, Xu C. Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems 2021; 58(Part B): 346–361. doi: 10.1016/j.jm¬sy.2020.06.017.
12) Del Giudice M, Osello A (editors). Handbook of research on developing smart cities based on digital twins. Hershey: IGI Global; 2021.
13) Tao F, Cheng J, Qi Q, et al. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Man¬ufacturing Technology 2018; 94: 3563–3576. doi: 10.1007/s00170-017-0233-1.
14) Wladimir Hofmann, Fredrik Branding, Implementation of an IoT- and Cloud-based Digital Twin for Real-Time Decision Support in Port Operations, IFAC-PapersOnLine, Volume 52, Issue 13, 2019, Pages 2104-2109, ISSN 2405-8963, https://doi.org/10.1016/j.ifacol.2019.11.516.
15) Kumar SAP, Madhumathi R, Chelliah PR, et al. A novel digital twin-centric approach for driver in¬tention prediction and traffic congestion avoidance. Journal of Reliable Intelligent Environments 2018;
16) T. Ambra and C. Macharis, "Agent-Based Digital Twins (ABM-Dt) In Synchromodal Transport and Logistics: the Fusion of Virtual and Pysical Spaces," 2020 Winter Simulation Conference (WSC), 2020, pp. 159-169, doi: 10.1109/WSC48552.2020.9383955.
17) Jiang W, Luo J. Graph neural network for traffic forecasting: A survey. Expert systems with applications. 2022 Nov 30;207:117921.
18) Kadar Muhammad Masum, M. Kalim Amzad Chy, I. Rahman, M. Nazim Uddin and K. Islam Azam, "An Internet of Things (IoT) based Smart Traffic Management System: A Context of Bangladesh," 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), 2018, pp. 418-422, doi: 10.1109/ICISET.2018.8745611.
19) Mohammed Sarrab, Supriya Pulparambil, Medhat Awadalla, Development of an IoT based real-time traffic monitoring system for city governance, Global Transitions, Volume 2 2020, Pages 230-245, ISSN 2589-7918, https://doi.org/10.1016/j.glt.2020.09.004.
20) https://www.google.com/maps
