The article discusses the necessity of building trust in AI for railway applications, highlighting that AI is currently limited to non‑safety critical uses due to stringent industry standards. It proposes focusing on three key areas—robustness, Operational Design Domain (ODD), and explainability—to meet compliance and safety requirements. By integrating these domains within a safe MLOps environment, the authors argue that regulatory acceptance and public confidence can be achieved, enabling broader AI adoption in mission‑critical railway systems.
By Lefebvre Renard Cl\'ement, L\'eb\'e Vincent, Da Silva Ribeiro Pereira Ricardo, Sundell Johan, Jaoul Arnaud Saiah Kenza, Mijatov\'ic Nenad
arXiv:2607. 17694v1 Announce Type: new Abstract: Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence.
By Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf
The study explores machine learning and deep learning techniques to predict travel time for transportation and logistics within supply chain systems. By leveraging extensive historical data, it aims to build an accurate model that estimates travel times for inventory movement. The research emphasizes the importance of precise travel time predictions for improving logistics consistency, performance, and planning across the supply chain.
By Balaji Venkateswaran
arXiv:2606. 06535v1 Announce Type: cross Abstract: Context.
By Faezeh Amou Najafabad, Markus Haug, Keerthiga Rajenthiram, Justus Bogner, Ilias Gerostathopoulos
arXiv:2607. 19676v1 Announce Type: new Abstract: Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the network edge.
By Wenbin Li, Zhongtian Liao, Bolin Liu, Yongjie Zhou, Jingling Wu, Xiaoyong Lin, Jing Chen
arXiv:2605. 02640v2 Announce Type: replace Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge.
By Ruta Binkyte, Ivaxi Sheth, Zhijing Jin, Mohammad Havaei, Bernhard Sch\"olkopf, Mario Fritz