AI-driven digital twin framework for sustainable smart city traffic management
Mohammad Nasar1 and Mohammad Abu Kausar2
Department of Computing and Informatics,Mazoon College,Muscat,Oman1
Department of Information Systems,University of Nizwa,Birkat Al Mouz,Oman2
Department of Information Systems,University of Nizwa,Birkat Al Mouz,Oman2
Corresponding Author : Mohammad Abu Kausar
Recieved : 06-September-2025; Revised : 24-May-2026; Accepted : 25-May-2026
Abstract
Urban road networks are increasingly experiencing traffic-related challenges, including congestion and increased fuel consumption, due to the continuous evolution of traffic demand. Traditional traffic engineering techniques are mostly reactive and are not well suited for real-time control. In this study, an artificial intelligence (AI)-based digital twin (DT) framework was introduced for sustainable urban traffic management. The framework integrates real-time internet of things (IoT) data, predictive machine learning (ML) models, and adaptive optimization within a closed-loop architecture. The real-time traffic DT was dynamically updated using field measurements to enable active traffic control. Sustainability considerations were incorporated through a multi-criteria formulation that simultaneously addressed congestion mitigation, energy consumption, and operational risk. The proposed methodology was validated using a large-scale Traffic4cast-based Simulation of Urban Mobility (SUMO) environment. The experimental results demonstrated significant reductions in congestion, energy consumption, and communication overhead. The feasibility of real-time operation at 0.5 Hz was confirmed through runtime and latency analyses. The findings highlight the potential of AI-enabled DT frameworks for scalable and sustainable smart-city traffic management.
Keywords
Artificial intelligence (AI), Digital twin (DT), Urban traffic management, Internet of things (IoT), Machine learning (ML), Sustainable smart cities.
Cite this article
Nasar M, Kausar MA. AI-driven digital twin framework for sustainable smart city traffic management. International Journal of Advanced Technology and Engineering Exploration. 2026;13(138):683-700. DOI : 10.19101/IJATEE.2025.121221243
References
[1]
Kušić K, Schumann R, Ivanjko E. A digital twin in transportation: real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics. Advanced Engineering Informatics. 2023; 55:1-17.
[2]
Hu C, Fan W, Zeng E, Hang Z, Wang F, Qi L, et al. Digital twin-assisted real-time traffic data prediction method for 5G-enabled internet of vehicles. IEEE Transactions on Industrial Informatics. 2021; 18(4):2811-9.
[3]
Chang X, Zhang R, Mao J, Fu Y. Digital twins in transportation infrastructure: an investigation of the key enabling technologies, applications, and challenges. IEEE Transactions on Intelligent Transportation Systems. 2024; 25(7):6449-71.
[4]
Li Y, Zhang W. Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion. Scientific Reports. 2023; 13(1):1-15.
[5]
Irfan MS, Dasgupta S, Rahman M. Toward transportation digital twin systems for traffic safety and mobility: a review. IEEE Internet of Things Journal. 2024; 11(14):24581-603.
[6]
Fang J, Yan D, Qiao J, Xue J, Yu H. DADA: driver attention prediction in driving accident scenarios. IEEE Transactions on Intelligent Transportation Systems. 2021; 23(6):4959-71.
[7]
Hu J, Wang S, Zhang Y, Wang H, Liu Z, Cao G. Safety-aware human-lead vehicle platooning by proactively reacting to uncertain human behaving. Transportation Research Part C: Emerging Technologies. 2025; 170:104941.
[8]
Ma C, Li X, Ma K, Zhang P, Long K, Chen S. Trajectory-based performance ranking system of low-level automated vehicles. In 26th international conference on intelligent transportation systems (ITSC) 2023 (pp. 5649-54). IEEE.
[9]
Abdessadak A, Ghennioui H, Thirion-moreau N, Elbhiri B, Abraim M, Merzouk S. Digital twin technology and artificial intelligence in energy transition: a comprehensive systematic review of applications. Energy Reports. 2025; 13:5196-218.
[10]
Almusaed A, Yitmen I. Architectural Reply for smart building design concepts based on artificial intelligence simulation models and digital twins. Sustainability. 2023; 15(6):1-20.
[11]
Ahmad MO, Ahad MA, Alam MA, Siddiqui F, Casalino G. Cyber-physical systems and smart cities in India: opportunities, issues, and challenges. Sensors. 2021; 21(22):1-25.
[12]
Yessef M, Hakam Y, Tabaa M, Alammar MM, Elbarbary ZM. Digital twin technology in smart cities: a step toward intelligent urban management. Energy Reports. 2025; 14:5539-57.
[13]
Zayed SM, Attiya GM, El-sayed A, Hemdan EE. A review study on digital twins with artificial intelligence and internet of things: concepts, opportunities, challenges, tools and future scope. Multimedia Tools and Applications. 2023; 82(30):47081-107.
[14]
Alnaser AA, Maxi M, Elmousalami H. AI-powered digital twins and internet of things for smart cities and sustainable building environment. Applied Sciences. 2024; 14(24):1-28.
[15]
Endla P, Kannan S, Adhiyaman M, Ramulu KP, Kumar BB, Hemamalini M. Sustainable artificial intelligence orchestrated urban planning models with digital twin technology for smart cities and green development. In international conference on sustainability innovation in computing and engineering (ICSICE 2024) 2025 (pp. 1044-58). Atlantis Press.
[16]
El-agamy RF, Sayed HA, Al AAM, Aljohani M, Elhosseini M. Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study. Artificial Intelligence Review. 2024; 57(6):1-54.
[17]
Sacoto-cabrera EJ, Perez-torres A, Tello-oquendo L, Cerrada M. IoT, AI, and digital twins in smart cities: a systematic review for a thematic mapping and research agenda. Smart Cities. 2025; 8(5):1-36.
[18]
Alvi M, Dutta H, Minerva R, Crespi N, Raza SM, Herath M. Global perspectives on digital twin smart cities: Innovations, challenges, and pathways to a sustainable urban future. Sustainable Cities and Society. 2025; 126:106356.
[19]
Bibri SE, Huang J. Artificial intelligence of things for sustainable smart city brain and digital twin systems: pioneering environmental synergies between real-time management and predictive planning. Environmental Science and Ecotechnology. 2025; 26:1-28.
[20]
Matei A, Cocoșatu M. Artificial internet of things, sensor-based digital twin urban computing vision algorithms, and blockchain cloud networks in sustainable smart city administration. Sustainability. 2024; 16(16):1-20.
[21]
Mayan A, Krishanveni S, Jothi B. AI enabled digital twin models to enhance security in smart cities. In international conference on intelligent computing and sustainable innovations in technology (IC-SIT) 2024 (pp. 1-6). IEEE.
[22]
Kausar MA, Yousif YK, Al BM, Nasar M, Al-dmour NA, Saeed AQ. Machine learning for IOT device security in smart homes. In 3rd international conference on business analytics for technology and security (ICBATS) 2025 (pp. 1-8). IEEE.
[23]
Prasath CA. AI-enabled digital twin framework for predictive maintenance in smart urban infrastructure. Journal of Smart Infrastructure and Environmental Sustainability. 2025; 2(1):1-10.
[24]
Kerkeni R, Khlif S, Mhalla A, Bouzrara K. Digital twin applied to predictive maintenance for industry 4.0. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems. 2024; 7(4):1-10.
[25]
Huzzat A, Anpalagan A, Khwaja AS, Woungang I, Alnoman AA, Pillai AS. A comprehensive review of digital twin technologies in smart cities. Digital Engineering. 2025; 4:1-26.
[26]
Cao J, Spulbar C, Eti S, Horobet A, Yüksel S, Dincer H. Innovative approaches to green digital twin technologies of sustainable smart cities using a novel hybrid decision-making system. Journal of Innovation & Knowledge. 2025; 10(1):1-11.
[27]
Thirion MR, Moghtadernejad S, Xu Y, Lounis Z, Chen Q. Digital twin platform based on an identity-semantic-interaction framework to improve bridge management workflows. Digital Engineering. 2026; 11:1-14.
[28]
Yazdiani A, Bafandkar S, Elmi A, Shafahi Y. An optimization framework for the time-dependent electric vehicle routing problem with shared mobility: a step toward smart cities. Digital Engineering. 2026: 100095.
[29]
Mongo M, Daidj N. Digital twin: a driver of sustainable smart cities? evidence from a bibliometric analysis. Environmental Economics and Policy Studies. 2025; 28:351-79.
[30]
Rosafalco L, Conti P, Manzoni A, Mariani S, Frangi A. EKF–SINDy: empowering the extended Kalman filter with sparse identification of nonlinear dynamics. Computer Methods in Applied Mechanics and Engineering. 2024; 431:1-22.
[31]
Krajzewicz D, Hertkorn G, Rössel C, Wagner P. SUMO (Simulation of Urban MObility)-an open-source traffic simulation. In proceedings of the 4th middle east symposium on simulation and modelling (MESM20002) 2002 (pp. 183-7).
[32]
Neun M, Eichenberger C, Martin H, Spanring M, Siripurapu R, Springer D, et al. Traffic4cast at neurips 2022–predict dynamics along graph edges from sparse node data: whole city traffic and eta from stationary vehicle detectors. In NeurIPS competition track 2023 (pp. 251-78). PMLR.
[33]
Shen J, Saini PK, Zhang X. Machine learning and artificial intelligence for digital twin to accelerate sustainability in positive energy districts. Data-driven Analytics for Sustainable Buildings and Cities: From Theory to Application. 2021:411-22.
[34]
Deb K, Pratap A, Agarwal S, Meyarivan TA. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 2002; 6(2):182-97.
[35]
Kopp M, Kreil D, Neun M, Jonietz D, Martin H, Herruzo P, et al. Traffic4cast at neurips 2020-yet more on the unreasonable effectiveness of gridded geo-spatial processes. In NeurIPS competition and demonstration track 2021 (pp. 325-43). PMLR.
[36]
Hoang TV. Impact of integrated artificial intelligence and internet of things technologies on smart city transformation. Journal of Technical Education Science. 2024; 19(1):64-73.
[37]
Matin A, Islam MR, Wang X, Huo H, Xu G. AIoT for sustainable manufacturing: overview, challenges, and opportunities. Internet of Things. 2023; 24:100901.
[38]
Long K, Ma C, Li H, Li Z, Huang H, Shi H, et al. AI-enabled digital twin framework for safe and sustainable intelligent transportation. Sustainability. 2025; 17(10):1-17.