TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility

• URL: http://arxiv.org/abs/2603.19474v1
• Date: Thu, 19 Mar 2026 21:11:42 GMT
• Title: TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility
• Abstract summary: Real-world trajectories are often sparse and feature unevenly distributed location points.<n>We introduce a novel diffusion model for trajectory recovery named TRACE.<n>TRACE reconstructs dense and continuous trajectories from sparse and incomplete inputs.
• Score: 34.4498141546852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real-world trajectories are often sparse and feature unevenly distributed location points. Recovering these trajectories into dense and continuous forms is essential but challenging, given their complex and irregular spatio-temporal patterns. In this paper, we introduce a novel diffusion model for trajectory recovery named TRACE, which reconstruct dense and continuous trajectories from sparse and incomplete inputs. At the core of TRACE, we propose a State Propagation Diffusion Model (SPDM), which integrates a novel memory mechanism, so that during the denoising process, TRACE can retain and leverage intermediate results from previous steps to effectively reconstruct those hard-to-recover trajectory segments. Extensive experiments on multiple real-world datasets show that TRACE outperforms the state-of-the-art, offering $>$26\% accuracy improvement without significant inference overhead. Our work strengthens the foundation for mobile and web-connected location services, advancing the quality and fairness of data-driven urban applications. Code is available at: https://github.com/JinmingWang/TRACE

Related papers

• TrajGPT-R: Generating Urban Mobility Trajectory with Reinforcement Learning-Enhanced Generative Pre-trained Transformer [22.847988014952488]
  Mobility trajectories are essential for understanding urban dynamics and enhancing urban planning.<n>Yet access to such data is frequently hindered by privacy concerns.<n>This research introduces a transformative framework for generating large-scale urban mobility trajectories.
  arXiv  Detail & Related papers  (2026-02-24T07:44:19Z)
• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation [36.880711201508085]
  Existing time-to-space methods often fail to effectively extract features in block-wise missing data scenarios.<n>This paper proposes a Spatiotemporal Attention Mixture of experts network named STAMImputer for traffic data imputation.<n>The result shows STAMImputer achieves significantly performance improvement compared with existing SOTA approaches.
  arXiv  Detail & Related papers  (2025-06-09T04:05:00Z)
• PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models [31.98187852227127]
  We propose a novel trajectory recovery model called PLMTrajRec.<n>It leverages the scalability of a pre-trained language model (PLM) and can be fine-tuned with only a limited set of dense trajectories.<n>To handle different sampling intervals in sparse trajectories, we first convert each trajectory's sampling interval and movement features into natural language representations.
  arXiv  Detail & Related papers  (2024-10-18T08:38:12Z)
• TrajWeaver: Trajectory Recovery with State Propagation Diffusion Model [22.45472148224533]
  We present a new recovery framework called TrajWeaver, which is able to recover dense and refined trajectories from the sparse raw ones. The core of TrajWeaver is a novel State propagation Model (SPDM), which introduces a new state propagation mechanism on top of the standard diffusion models. Extensive experiments show that the proposed TrajWeaver can recover from raw trajectories of various lengths, sparsity levels and heterogeneous travel modes.
  arXiv  Detail & Related papers  (2024-09-01T06:42:19Z)
• TraceMesh: Scalable and Streaming Sampling for Distributed Traces [51.08892669409318]
  TraceMesh is a scalable and streaming sampler for distributed traces. It accommodates previously unseen trace features in a unified and streamlined way. TraceMesh outperforms state-of-the-art methods by a significant margin in both sampling accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-06-11T06:13:58Z)
• LightTR: A Lightweight Framework for Federated Trajectory Recovery [15.767265437130586]
  We aim to recover a high-sampled trajectory based on the low-sampled trajectory in free space without road network information. We propose a lightweight framework, LightTR, for federated trajectory recovery based on a client-server architecture.
  arXiv  Detail & Related papers  (2024-05-06T12:20:55Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model [44.490978394267195]
  We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
  arXiv  Detail & Related papers  (2023-04-23T08:42:45Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• AttnMove: History Enhanced Trajectory Recovery via Attentional Network [15.685998183691655]
  We propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-03T15:45:35Z)

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.