AI- and Digital Twin-Driven Intelligent Diagnostics and Predictive Maintenance for Transportation Systems

Dear Colleagues,

Modern transportation systems—spanning complex vehicles and critical infrastructures—underpin safety, mobility, and the global economy. As these cyber–physical assets grow in scale and complexity, maintenance must evolve from reactive policies to data-driven Prognostics and Health Management (PHM). The convergence of Artificial Intelligence (AI) and Digital Twins (DTs) offers a powerful pathway: AI leverages multi-modal sensing and operations data for accurate diagnostics and Remaining Useful Life (RUL) prediction, while DTs provide high-fidelity virtual counterparts for real-time monitoring, “what-if” maintenance simulation, and risk-aware decision-making.

This Special Issue aims to advance the science and practice of AI–DT-enabled PHM for transportation systems by foregrounding the following key research challenges: seamless AI–DT integration and real-time synchronization; explainable and trustworthy decision support; uncertainty quantification and propagation across the sensing–modeling–decision chain; benchmarking and reproducibility; and scalable deployment across heterogeneous fleets and infrastructures. We particularly welcome methodologies that address multi-modal data fusion, edge and federated learning under privacy/latency constraints, digital-twin fidelity assessment and verification/validation, lifecycle management, and sustainability evaluation of maintenance policies.

We invite contributions that develop foundational theory, algorithmic innovation, and rigorous applications across vehicles (railway, automotive, aviation, maritime) and infrastructures (tracks, bridges, tunnels, roadways, depots, communications). Contributions should include novel hybrid AI–DT architectures; real-time prognostics and risk-informed scheduling; cost–benefit and lifecycle analyses; or interoperable frameworks that align with standards. We also welcome case studies demonstrating measurable safety, reliability, and availability gains.

Topics of interest, include but are not limited to, the following:

• Hybrid AI–DT architectures for diagnostics, prognostics, and predictive maintenance.
• Real-time synchronization between physical assets and DTs; streaming analytics and online learning.
• Multi-modal data fusion (vibration, acoustic, image/video, operational logs, environmental and network data).
• Edge and federated learning for privacy-preserving, low-latency deployment at scale.
• RUL prediction, health indicators, and risk-aware/uncertainty-aware decision-making (UQ).
• Trustworthy/Explainable AI (XAI), robustness to domain shift, and safety assurance.
• Digital Twin modeling, calibration, fidelity assessment, V&V, and co-simulation.
• Condition-based maintenance (CBM), maintenance optimization, and scheduling under constraints.
• Benchmarking datasets, evaluation protocols, and reproducible baselines.
• Lifecycle management and sustainability assessment of maintenance strategies.

Dr. Xiaoxi Hu
Dr. Junyu Qi
Dr. Dandan Peng
Dr. Jiusi Zhang
Guest Editors

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• transportation systems
• artificial intelligence (AI)
• digital twins (DT)
• prognostics and health management (PHM)
• condition-based maintenance (CBM)
• predictive maintenance
• remaining useful life (RUL)
• explainable AI (XAI)
• data fusion
• edge and federated learning