Transferable Human Mobility Network Reconstruction with neuroGravity

• URL: http://arxiv.org/abs/2604.23678v1
• Date: Sun, 26 Apr 2026 12:33:07 GMT
• Title: Transferable Human Mobility Network Reconstruction with neuroGravity
• Abstract summary: We develop neuroGravity, a physics-informed deep learning model that reconstructs mobility flows from limited observations and transfers to unobserved cities.<n>Using only urban facility and population distributions, we find that neuroGravity's regional representations strongly correlate with socioeconomic livability and status.<n>We generate mobility flow proxies for over 1,200 cities worldwide, highlighting neuroGravity's potential to mitigate critical data shortages in resource-limited, underdeveloped areas.
• Score: 33.130165516944516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing mobility networks from publicly available data. Here we develop neuroGravity, a physics-informed deep learning model that reliably reconstructs mobility flows from limited observations and transfers to unobserved cities. Using only urban facility and population distributions, we find that neuroGravity's regional representations strongly correlate with socioeconomic and livability status, offering scalable proxies for costly surveys. Furthermore, we uncover that spatial income segregation plays a key role in model transferability: mobility networks are most reliably reconstructed when target cities share similar segregation levels with the source. We design an index to quantify this segregation and accurately predict transferability. Finally, we generate mobility flow proxies for over 1,200 cities worldwide, highlighting neuroGravity's potential to mitigate critical data shortages in resource-limited, underdeveloped areas.

Related papers

• Inferring Urban Mobility Interactions from Aggregated Dynamics [45.73534370352232]
  Real-time urban governance depends not only on knowing where people are, but on how they move between places.<n>Here we show that this directional structure need not be observed to be known.<n>We infer future OD flows from area-level counts alone across twelve mobility datasets from cities in the United States and China.
  arXiv  Detail & Related papers  (2026-09-07T11:13:51Z)
• MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models [0.37650120899162426]
  MoRAX is a framework for augmenting geospatial embeddings with functional structure derived from human mobility.<n>Across four target cities spanning two countries, the MoRAX teacher model consistently outperforms GFMs and strong urban representation baselines.
  arXiv  Detail & Related papers  (2026-08-18T14:44:41Z)
• GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models [46.28228608289378]
  The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.<n>This research proposes a network-agnostic initialization layer, termed Geometrically Unconstrained Inductive Demand EmbeDding.<n>This framework standardizes the input space regardless of network scale.<n>It provides a versatile blueprint for generalized origin-destination spatial problems, such as freight logistics and multimodal network optimization.
  arXiv  Detail & Related papers  (2026-07-21T16:41:59Z)
• Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models [44.99833362998488]
  We propose using commute information records as a reliable and comprehensive source to construct mobility networks across cities.<n>We show that mobility network structures provide significant predictive performance without considering any node features.<n>Our experiments in 12 major U.S. cities show the proposed model outperforms previous conventional machine learning models.
  arXiv  Detail & Related papers  (2025-07-05T12:38:59Z)
• From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places [0.30693357740321775]
  This paper advocates for a new class of spatial foundation models that integrate geolocation semantics with human mobility across multiple scales.<n>Our goal is to guide the development of scalable, context-aware models for next-generation geospatial intelligence.
  arXiv  Detail & Related papers  (2025-06-17T14:27:24Z)
• Graph Network Modeling Techniques for Visualizing Human Mobility Patterns [8.537183852577687]
  We develop a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics.<n>We demonstrate how mobility data collected from trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
  arXiv  Detail & Related papers  (2025-04-04T02:21:44Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• COLA: Cross-city Mobility Transformer for Human Trajectory Simulation [44.157114416533915]
  We develop a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework. COLA divides the Transformer into the private modules for city-specific characteristics and the shared modules for city-universal mobility patterns. Our implemented cross-city baselines have demonstrated its superiority and effectiveness.
  arXiv  Detail & Related papers  (2024-03-04T07:45:29Z)
• Generating Synthetic Mobility Networks with Generative Adversarial Networks [3.3528341717949197]
  MoGAN is a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models.
  arXiv  Detail & Related papers  (2022-02-22T16:59:35Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems [42.08603087208381]
  We argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. We propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. We show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks.
  arXiv  Detail & Related papers  (2021-04-23T06:42:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.