arXiv AI

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

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

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
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
arXiv AI
Jun 2

Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support

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
arXiv Machine Learning
Jun 17

Public transit gains and spatially uneven travel demand changes after NYC congestion pricing

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
arXiv AI
Jun 16

Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility

arXiv:2606. 15251v1 Announce Type: cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation.

By Simon Kohaut, Felix Divo, Julius Hahnewald, Benedict Flade, Julian Eggert, Kristian Kersting, Devendra Singh Dhami
arXiv Machine Learning
Jun 25

Cellular Predictions on the Move: What about Data?

arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.

By Natalia Vesselinova, Pauliina Ilmonen