What does this research mean for the field?

The integration of Digital Twin and machine learning technologies in Intelligent Transportation Systems can improve traffic efficiency, reduce congestion, and enhance safety, despite ongoing challenges with scalability, communication delays, and real-world validation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review the integration of Digital Twin technologies and machine learning control within Intelligent Transportation Systems for smart, adaptive traffic management.

August 29, 2026IoTOpen Access

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

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Authors

TTThit TunAuckland University of Technology HSHakilo SabitAuckland University of Technology

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Implication

Literature review demonstrates digital twin and machine learning integration optimizes traffic management in urban transit systems, highlighting unresolved latency and scalability challenges.

Key Points

To review the integration of Digital Twin technologies and machine learning control within Intelligent Transportation Systems for smart, adaptive traffic management.
Synthesized recent literature across conventional, optimization-based, and machine learning traffic control methodologies.
Evaluated key enabling technologies, including reinforcement learning, Graph Neural Networks, vehicle-to-everything communication, and edge computing for real-time monitoring and control.
Digital twin-enabled architectures improve traffic efficiency, reduce urban congestion, and support sustainable mobility through synchronized, data-driven signal control.
Major implementation bottlenecks persist regarding communication delays, sensor uncertainty, computational overhead, and validation in complex real-world environments.

Cite This Study

Tun et al. (2026) studied this question.

synapsesocial.com/papers/6a9299338e5d7d1fc0c10f3f https://doi.org/10.3390/iot7030069

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Implication

Literature review demonstrates digital twin and machine learning integration optimizes traffic management in urban transit systems, highlighting unresolved latency and scalability challenges.

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

Discussion

Member takes

Implication

Key Points

Cite This Study

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

Discussion

Member takes

Implication

Key Points

Cite This Study

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