arXiv AI July 1, 2026 By Zhengxuan Wang, Haohan He, Mengying Zhou

Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems

arXiv:2606. 31207v1 Announce Type: new Abstract: The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are often sparsely represented in public mobility datasets.

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

arXiv AI

6d ago

Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

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

llms fine-tuning

arXiv Machine Learning

Aug 4

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

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

computer-vision

arXiv AI

Jun 10

Deep Generative Model for Human Mobility Behavior

arXiv:2510. 06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health.

By Ye Hong, Yatao Zhang, Konrad Schindler, Martin Raubal

diffusion

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

fine-tuning benchmarks

arXiv AI

Jun 12

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.

By Siyu Li, Toan Tran, Lingyi Zhao, Khurram Shafique, Li Xiong

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 4

Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent

arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.

By Linyao Chen, Qinlao Zhao, Zechen Li, Mingming Li, Likun Ni, Jinyu Chen, Yuhao Yao, Xuan Song, Noboru Koshizuka, Hiroki Kobayashi

llms agents safety

Related stories

Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

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

Deep Generative Model for Human Mobility Behavior

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

TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation

Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent