RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer

• URL: http://arxiv.org/abs/2211.13234v1
• Date: Wed, 23 Nov 2022 11:28:32 GMT
• Title: RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
• Authors: Yuqi Chen, Hanyuan Zhang, Weiwei Sun, Baihua Zheng
• Abstract summary: We propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It then introduces a Sub-Graph Generation module to represent each GPS point as a sub-graph structure of the road network around the GPS point.
• Score: 15.350300338463969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints.We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories. Currently, most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and lack spatial consistency. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a Sub-Graph Generation module to represent each GPS point as a sub-graph structure of the road network around the GPS point. It then introduces a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features. It finally forwards the outputs of encoder model into a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach.

Related papers

• GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers [53.80009458891537]
  Cross-view video geo-localization aims to derive GPS trajectories from street-view videos by aligning them with aerial-view images. Current CVGL methods use camera and odometry data, typically absent in real-world scenarios. We propose GAReT, a fully transformer-based method for CVGL that does not require camera and odometry data.
  arXiv  Detail & Related papers  (2024-08-05T21:29:33Z)
• Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery [21.911875343270683]
  Recovering missing GPS points in a sparse trajectory could offer deep insights into users' moving behaviors in intelligent transportation systems. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory. We propose a Micro-Macro- Spatial Graph-Decoder (MM-STGED) to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism.
  arXiv  Detail & Related papers  (2024-04-29T22:54:35Z)
• Surrogate Modeling of Trajectory Map-matching in Urban Road Networks using Transformer Sequence-to-Sequence Model [1.3812010983144802]
  This paper introduces a deep-learning model, specifically the transformer-based encoder-decoder model, to perform as a surrogate for offline map-matching algorithms. The model is trained and evaluated using GPS traces collected in Manhattan, New York.
  arXiv  Detail & Related papers  (2024-04-18T18:39:23Z)
• A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery [27.347708962204713]
  This work is dedicated to offering a more robust trajectory recovery for complex trajectories. We propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training. The results prove that our method learns better representations for trajectory recovery, with 5.22% higher F1-score overall and 8.16% higher F1-score for complex trajectories particularly.
  arXiv  Detail & Related papers  (2023-11-05T12:20:39Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT) [39.055098754375294]
  We introduce a novel setup called a-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. We also present a novel framework called Spatial Contrastive Pre-Training ( SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads. The SCPT framework also incorporates a new layer, named the gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones.
  arXiv  Detail & Related papers  (2023-05-09T07:56:26Z)
• 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)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Learning Spatio-Temporal Transformer for Visual Tracking [108.11680070733598]
  We present a new tracking architecture with an encoder-decoder transformer as the key component. The whole method is end-to-end, does not need any postprocessing steps such as cosine window and bounding box smoothing. The proposed tracker achieves state-of-the-art performance on five challenging short-term and long-term benchmarks, while running real-time speed, being 6x faster than Siam R-CNN.
  arXiv  Detail & Related papers  (2021-03-31T15:19:19Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

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.