Trustworthy data and ml operations improve transportation and logistics
Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics
Artificial IntelligenceSoftware Engineering
Summary
Transportation and delivery systems are becoming smarter by using data and machine learning, but these systems need to be reliable and trustworthy. The authors review how managing data and AI tools carefully can make these systems work better and be more dependable. They also look at existing challenges and suggest ways to build more trustworthy AI systems for real-world transportation and logistics. Their work helps guide future research and practical improvements.
What this means in practice
- •For transportation system operators: Use insights from trustworthy DataOps and MLOps to enhance the reliability of AI-driven traffic and transit management systems.
- •For logistics service providers: Adopt best practices in DataOps and MLOps to improve decision-making and operational efficiency in delivery and supply chain processes.
A survey. It maps existing work.
Authors
Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
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.