Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control
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Tun, Thit
Sabit, Hakilo
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MDPI AG
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This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management. It examines the role of key enabling technologies, including the Internet of Things (IoT), machine learning (ML), deep learning (DL), reinforcement learning (RL), Graph Neural Networks (GNNs), vehicle-to-everything (V2X) communication, and edge computing. These technologies support real-time traffic monitoring, traffic prediction, and adaptive control in ITS. The review synthesizes recent research on conventional traffic control methods, optimization-based approaches, learning-based techniques, and DT-enabled traffic management solutions. Particular attention is given to the integration of DTs with intelligent traffic signal control, real-time synchronization, multi-intersection coordination, communication latency, sensing uncertainty, and scalability. The reviewed literature demonstrates the potential of DT-enabled ITS to improve traffic efficiency, reduce congestion, enhance transportation safety, and support sustainable mobility through data-driven decision-making. However, significant challenges remain regarding communication delays, sensor and data uncertainty, computational complexity, scalability, and validation under realistic urban conditions. Based on the reviewed literature, this paper identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS architectures for next-generation smart cities.
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35 Commerce, Management, Tourism and Services, 40 Engineering, 4005 Civil Engineering, 46 Information and Computing Sciences, 3509 Transportation, Logistics and Supply Chains, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Data Science, 11 Sustainable Cities and Communities, ITS, traffic control, Digital Twin, Graph Neural Networks, reinforcement learning
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IoT, ISSN: 2624-831X (Print); 2624-831X (Online), MDPI AG, 7(3), 69-69. doi: 10.3390/iot7030069
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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Except where otherwise noted, this item's license is described as Creative Commons Attribution (CC BY) license