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.
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.
The paper "Rethinking Multi-View Fusion for Urban Representation Learning" introduces PRISM, a framework that addresses two key issues in urban data fusion: varying modality utility across regions and persistent gradient imbalance among heterogeneous objectives. PRISM employs sparse Mixture-of-Experts routing for region-specific view allocation and a PID-inspired regulation mechanism to reduce gradient conflicts during training. Experiments on three large-scale urban datasets demonstrate that PRISM achieves competitive performance while offering more stable optimization and balanced region representations.
arXiv:2510. 14819v3 Announce Type: replace-cross Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis.
The paper introduces the Activity Chain Encoder (ACE), a self‑supervised deep learning model that transforms passively collected mobile phone location data into daily activity representations. ACE integrates pre‑trained urban embeddings, visit timing, and duration, using a Transformer to capture the sequential structure of stays, and is trained via masked activity modelling and contrastive learning without explicit activity labels. The resulting user‑level profiles are clustered and interpreted with temporal‑functional patterns and Census demographics, revealing six distinct weekday activity‑pattern groups in London that differ in daily rhythms, urban contexts, and demographic characteristics.
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.
arXiv:2609.15305v1 Announce Type: cross Abstract: Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use informati...
The paper introduces TransMod, a unified framework for forecasting urban mobility demand across multiple transportation modes. It creates a shared zone-level spatial representation to align systems with different spatial granularities, reducing structural mismatch and distributional shift. TransMod then learns transferable spatio-temporal patterns from data-rich source modes and adapts them to data-scarce target modes, improving forecasting performance when target data is limited.
arXiv:2607. 24885v1 Announce Type: cross Abstract: Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility.
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.
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.
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.
SeoulMMOD is a new large‑scale public benchmark that provides three years of hourly origin‑destination flow data for six urban travel modes across 25 districts and 426 sub‑districts in Seoul, along with travel‑time, travel‑distance, calendar, rainfall, and point‑of‑interest signals. The dataset enables city‑wide multimodal mobility analysis and supports the evaluation of 14 forecasting baselines under a common protocol. The benchmark reveals that models effective at the district level may not scale to sub‑district forecasting and that naive joint multi‑mode training does not always improve accuracy.