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
The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
By Yidi Wang, Yunhe Zhang, Bangchao Deng, Dingqi Yang, Pengyang Wang
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: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
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
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