arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
By Jinpeng Chen, Ziyu Yu, Tao Wang, Jun Ma, Hongbo Gao, Senzhang Wang, Zufeng Zhang, Kaimin Wei
LE4Mob is a new location embedding framework that learns inductive, distance‑aware representations from geographic context, enabling it to encode unseen locations and preserve spatial relationships. It builds on contrastive language‑location pre‑training and adds a regularisation objective that encourages the embedding space to reflect geographic distance. Experiments on next‑location prediction and commuter flow generation across multiple datasets show that LE4Mob outperforms strong baselines, especially in inductive settings and when downstream models use direct interactions between location embeddings.
By Xinglei Wang, Stephen Law, Zichao Zeng, Junyuan Liu, Guangsheng Dong, Tao Cheng
MoRAX is a lightweight framework that augments geospatial foundation model embeddings with functional structure derived from human mobility data. By incorporating mobility flows, MoRAX preserves the coverage and consistency of existing geospatial models while adding information about functional connectivity among urban regions, enabling zero‑shot deployment in unseen cities. Experiments across four cities in two countries show that the MoRAX teacher model outperforms baseline geospatial models on eight socioeconomic and environmental prediction tasks, and the student model—without direct mobility input—approaches the teacher’s performance.
By Ya Wen, Jixuan Cai, Yulun Zhou, Alec Kirkley
arXiv:2606. 09086v1 Announce Type: new Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations.
By Jie Zhao, Xianqi Dai, Jie Feng, Huandong Wang, Yong Li
MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.
By Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou
The paper introduces Mobility Stream-Structure Synergy (MoSS), a method that fuses two complementary views of mobility data—an hourly inflow/outflow Sequence view and a Structure view derived from zigzag persistence diagrams—to capture temporal dynamics and evolving regional connectivity. MoSS employs a synergy module that extracts higher‑order representations from the co‑occurrence of these views, moving beyond additive fusion. Experiments on New York City and Chicago demonstrate that MoSS outperforms existing baselines on three downstream tasks using only mobility data.
By Namwoo Kim, Jeeyun Chang, Kanghoon Lee, Yoonjin Yoon