arXiv Machine Learning By Andr\'as J. Moln\'aar, Csaba I. Sidl\'o, Rita R\'onai, Domonkos R\'ozsay

Paris as a 15-Minute City: An Explainable AI Perspective

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
1d ago

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

arXiv:2608. 14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults.

By Houhao Liang, Kresimir Friganovic, Joanne Kua, Noor Hafizah Ismail, Su Su, Bryan Yijia Tan, Navrag B. Singh, Panos Mavros
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 Machine Learning
Aug 11

TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

arXiv:2507. 00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs.

By Massimiliano Luca, Ciro Beneduce, Bruno Lepri