arXiv AI By Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf

Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities

Read the original on arXiv AI →

arXiv:2607. 17694v1 Announce Type: new Abstract: Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
2d ago

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

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 AI
Jul 3

Autonomous discovery of traffic laws with AI traffic scientists

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 AI
Aug 19

CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

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 AI
Jul 7

From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support

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