The paper reviews Trustworthy Data and Machine Learning Operations (DataOps and MLOps) for Intelligent Transportation Systems and Logistics (ITS&L). It identifies gaps in current literature, discusses the complexities, key components, tools, practical insights, and case studies relevant to ITS&L, and examines methods to strengthen trustworthiness in AI applications. The authors conclude by outlining ongoing challenges and future prospects, positioning the work as a resource for researchers, industry practitioners, and policymakers.
By Antonio Emanuele Cin\`a, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
arXiv:2607. 01639v1 Announce Type: new Abstract: Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a scientific basis for transportation planning, management and control.
By Xingyuan Dai, Yue Liu, Xiaoyan Gong, Qinghai Miao, Junyou Shang, Yutong Wang, Chao Guo, Yonglin Tian, Yizhang Chai, Chao Xiang, Yisheng Lv, Fei-Yue Wang
arXiv:2608. 00815v1 Announce Type: new Abstract: The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify.
By Andr\'as J. Moln\'aar, Csaba I. Sidl\'o, Rita R\'onai, Domonkos R\'ozsay
CityReal is a modular framework that uses large language model agents to simulate human-aligned urban behavior. It models agents as intention-driven decision makers who pursue coherent mobility and activity plans, learning habits and preferences over time. By training textual adapters to align agent decisions with observed population statistics, CityReal improves realism at both micro and macro levels and can scale to tens of thousands of agents for analyzing crowd density, place popularity, mobility flows, and well‑being under various urban scenarios.
By Nicolas Bougie, Xiaotong Ye, Narimasa Watanabe
arXiv:2607. 03394v1 Announce Type: new Abstract: Real time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation.
By Thiago Andrade, Shazia Tabassum, Miguel E. P. Silva, Ricardo Dinis, Joao Gama
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
By Xishun Liao, Qinhua Jiang, Brian Yueshuai He, Yifan Liu, Chenchen Kuai, Jiaqi Ma
arXiv:2608. 11080v1 Announce Type: new Abstract: Rail transit systems play a vital role in urban mobility and economic development.
By Shutong Zhu, Tianxing Wu, Runfeng Liu, Yuang Gu, Xuan He, Yuan Zhu
arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.
By Siyan Li, Zehao Wang, Jiachen Li, Kanok Boriboonsomsin, Matthew J. Barth, Guoyuan Wu
An expert in behavioral science and transportation, Zhao combines these studies with AI and public policy to address some of the most urgent challenges facing cities.
By Maria Iacobo | School of Architecture and Planning
New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions. Because such policies generate spillovers across modes and locations, credible control groups are difficult to construct.
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:2606. 17530v1 Announce Type: cross Abstract: New York City implemented the nation's first cordon-based congestion pricing program in January 2025, providing an opportunity to evaluate how system-wide urban mobility responds to large-scale pricing interventions.
By Donghang Li, Dingyi Zhuang, Yunlin Li, Chenan Shen, Nina Cao, Yunhan Zheng, Shenhao Wang, Jinhua Zhao