Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

2026-10-01

AI Summary

This study addresses the lack of trustworthiness in data and machine learning operations within Intelligent Transportation Systems and Logistics (ITS&L) by systematically integrating trustworthy AI mechanisms into ITS&L operational workflows for the first time. Leveraging DataOps and MLOps technology stacks alongside trustworthiness assessment methodologies and domain-specific tools, this work provides a comprehensive review of key components, toolchains, and practical case studies in this field. The research bridges existing gaps in the literature, establishes a foundational framework for trustworthy operations, and outlines future research directions. Ultimately, it serves as a critical resource for researchers, practitioners, and policymakers, facilitating the development of efficient, sustainable, and trustworthy intelligent transportation systems.

Abstract

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.

Problem

• Intelligent Transportation Systems
• Logistics
• DataOps
• MLOps
• Trustworthy AI

Innovation

• Trustworthy AI
• DataOps
• MLOps
• Intelligent Transportation Systems
• Logistics