AI 中文总结
本文综述了智能交通与物流领域中的可信数据运维与机器学习运维，指出文献空白，探讨其关键组件、工具与案例，并强调AI可信性，为研究人员和从业者提供基础资源并指明未来研究方向。
AI 中文摘要
智能交通系统与物流（ITS\&L）的快速发展已成为现代社会经济的基石，其高度依赖于数据、人工智能（AI），尤其是机器学习（ML）的集成。本文对ITS\&L领域中的可信数据运维（DataOps）与机器学习运维（MLOps）进行了全面综述，强调了它们在提升交通与物流服务的效率、可靠性及决策精度方面的重要性。我们首先指出现有文献中的空白，为我们的贡献提供清晰的背景。随后，我们探讨DataOps和MLOps的复杂性，讨论其必要性、关键组件、可用工具、实践见解以及与ITS\&L相关的案例研究。此外，我们处理AI应用中的可信性这一关键问题，审视旨在增强AI系统（尤其是在真实世界的ITS\&L场景中）信心的方法和工具。本文最后讨论了这一快速发展领域中持续存在的挑战和未来前景，旨在为研究人员、行业从业者和政策制定者提供重要资源。总体而言，这项工作不仅为ITS\&L中的DataOps和MLOps建立了基础性理解，也为开发更高效、可持续和可信的智能交通与物流系统的进一步研究和创新指明了道路。
英文摘要
The rapid evolution of Intelligent Transportation Systems and Logistics (ITS\&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review of Trustworthy Data and Machine Learning Operations (DataOps and MLOps) in the ITS\&L domain, underscoring their importance in improving efficiency, reliability, and decision-making precision within transportation and logistics services. We begin by identifying gaps in current literature, offering clear context for our contribution. Subsequently, we explore the complexities of DataOps and MLOps, discussing their necessity, key components, available tools, practical insights, and case studies relevant to ITS\&L. Additionally, we address the critical issue of Trustworthiness in AI applications, examining methods and tools designed to strengthen confidence in AI systems - especially in real-world ITS\&L scenarios. The paper concludes with a discussion of persisting challenges and future prospects in this rapidly advancing field, aiming to serve as a vital resource for researchers, industry practitioners, and policy makers. Overall, this work not only establishes a foundational understanding of DataOps and MLOps in ITS\&L but also charts a path for further research and innovation in developing more efficient, sustainable, and trustworthy intelligent transportation and logistics systems.
CommentsPaper accepted at accepted at IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2026.3711756
Journal refIEEE Transactions on Intelligent Transportation Systems, 2026
DOI：10.1109/TITS.2026.3711756.
