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.
By Zhengxuan Wang, Haohan He, Mengying Zhou
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: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
The study investigates whether large language models (LLMs) can predict neighborhood-level human mobility without training data. Using anonymized Cuebiq data across four U.S. metropolitan areas, the authors compare zero‑shot LLM predictions to supervised baselines for various mobility outcomes and assess structural alignment with empirical trends. Results show supervised models outperform LLMs (average accuracy 0.580 vs. 0.435), with LLMs relying on coarse, stable priors that may exhibit biased treatment of protected-group predictors.
By Saad Mohammad Abrar, Eesha Kurella, Arnav Dadarya, Naman Awasthi, Kazi Tasnim Zinat, Vanessa Frias-Martinez
arXiv:2603.05581v2 Announce Type: replace-cross
Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...
By Olaf Yunus Laitinen Imanov
BER-PEF is a Bayes‑error‑rate‑based framework that transforms BER estimation into human mobility predictability estimation, enabling comparison of different estimators even when ground truth predictability is not observable. It maps various data types—symbolic sequences, numeric trajectories, contextual features, and learned representations—into a shared feature–label space and evaluates estimator outputs along controlled perturbation curves against a common predictability reference interval. Experiments on datasets such as Foursquare NYC/TKY, GeoLife, and T‑Drive show that several BER‑based estimators outperform existing methods on symbolic sequences and numeric trajectories, and that aggregating evidence across multiple perturbation levels yields a more reliable basis for selecting estimators.
By En Xu, Jingtao Ding, Zhiwen Yu, Yong Li