DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling

• URL: http://arxiv.org/abs/2606.09086v1
• Date: Mon, 08 Jun 2026 06:32:49 GMT
• Title: DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
• Abstract summary: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone.<n>We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives.<n>Our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity.
• Score: 20.859733498481173
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key challenge is to translate semantic temporal signals into temporally coherent OD patterns while preserving the inherent spatial heterogeneity of urban regions. We propose DynaOD, a semantic-driven framework that models temporal dynamics through two complementary perspectives: discrete directional trends that characterize qualitative shifts in urban activity patterns, and continuous temporal evolution that captures how such shifts unfold over time. By jointly encoding these temporal semantics, the framework constructs time-varying region representations that condition pretrained static OD generators in a lightweight and plug-and-play fashion. This modular design further supports scalable deployment and cross-city transferability. Extensive experiments on large-scale real-world datasets show that our method consistently outperforms representative baselines in both predictive accuracy and distributional fidelity. Code is publicly available at https://github.com/csjiezhao/DynaOD.

Related papers

• DSAR: Dual-Stream Autoregressive Modeling of Temporal Cloth Dynamics for Photorealistic Animatable Avatars [7.198815179389743]
  We introduce a dual-stream autoregressive framework that explicitly models both observable geometric information and implicit internal state.<n> Experiments on challenging datasets demonstrate significant improvements in rendering quality, temporal consistency, and generalization to motion patterns beyond training distributions.
  arXiv  Detail & Related papers  (2026-08-11T05:19:21Z)
• Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting [17.98687936773676]
  We introduce a continuous-time Koopman framework that models latent evolution through numerical integration schemes.<n>By allowing variable timesteps at inference, the method demonstrates robustness to temporal resolution and generalizes beyond training regimes.<n>We evaluate the approach on classical CFD benchmarks and report accuracy, stability, and extrapolation properties.
  arXiv  Detail & Related papers  (2026-02-02T21:33:07Z)
• Learning Multi-Modal Mobility Dynamics for Generalized Next Location Recommendation [51.00494428978262]
  We leverage multi-modal spatial-temporal knowledge to characterize mobility dynamics for the location recommendation task.<n>First, we construct a unified spatial-temporal relational graph (STRG) for multi-modal representation.<n>Second, we design a gating mechanism to fuse spatial-temporal graph representations of different modalities.
  arXiv  Detail & Related papers  (2025-12-27T14:23:04Z)
• Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching [16.17115009663765]
  We introduce textbfPAFM, a framework that models perturbed trajectories to ensure stable and structurally consistent time series generation.<n>The framework incorporates perturbation-guided training to simulate localized disturbances and leverages a dual-path velocity field to capture trajectory deviations under perturbation.<n>In experiments on both unconditional and conditional generation tasks, PAFM consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-11-18T13:30:56Z)
• RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion [64.49056527678606]
  We propose a Token-wise Attention integrated into not only the U-Net diffusion model but also the radar-temporal encoder.<n>Unlike prior approaches, our method integrates attention into the architecture without incurring the high resource cost typical of pixel-space diffusion.<n>Our experiments and evaluations demonstrate that the proposed method significantly outperforms state-of-the-art approaches, robustness local fidelity, generalization, and superior in complex precipitation forecasting scenarios.
  arXiv  Detail & Related papers  (2025-10-16T17:59:13Z)
• Kuramoto Orientation Diffusion Models [67.0711709825854]
  Orientation-rich images, such as fingerprints and textures, often exhibit coherent angular patterns.<n>Motivated by the role of phase synchronization in biological systems, we propose a score-based generative model.<n>We implement competitive results on general image benchmarks and significantly improves generation quality on orientation-dense datasets like fingerprints and textures.
  arXiv  Detail & Related papers  (2025-09-18T18:18:49Z)
• Transformer with Koopman-Enhanced Graph Convolutional Network for Spatiotemporal Dynamics Forecasting [12.301897782320967]
  TK-GCN is a two-stage framework that integrates geometry-aware spatial encoding with long-range temporal modeling.<n>We show that TK-GCN consistently delivers superior predictive accuracy across a range of forecast horizons.
  arXiv  Detail & Related papers  (2025-07-05T01:26:03Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes.<n>Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting [11.680589359294972]
  We propose TrendGCN to address the challenge of balancing dynamics and robustness. Our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts.
  arXiv  Detail & Related papers  (2022-08-05T09:36:55Z)

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.