A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery

• URL: http://arxiv.org/abs/2208.07739v1
• Date: Tue, 16 Aug 2022 13:21:46 GMT
• Title: A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery
• Authors: Yuting Ding, Di Wu
• Abstract summary: Missing is an inevitable and common problem in data-driven intelligent (ITS) This paper proposes an Aim-temporal traffic data completion method based on hidden feature analysis. The results show that the model can accurately estimate the continuous missing data.
• Score: 3.84562917529518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Missing data is an inevitable and common problem in data-driven intelligent transportation systems (ITS). In the past decade, scholars have done many research on the recovery of missing traffic data, however how to make full use of spatio-temporal traffic patterns to improve the recovery performance is still an open problem. Aiming at the spatio-temporal characteristics of traffic speed data, this paper regards the recovery of missing data as a matrix completion problem, and proposes a spatio-temporal traffic data completion method based on hidden feature analysis, which discovers spatio-temporal patterns and underlying structures from incomplete data to complete the recovery task. Therefore, we introduce spatial and temporal correlation to capture the main underlying features of each dimension. Finally, these latent features are applied to recovery traffic data through latent feature analysis. The experimental and evaluation results show that the evaluation criterion value of the model is small, which indicates that the model has better performance. The results show that the model can accurately estimate the continuous missing data.

Related papers

• 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)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• TRIAGE: Characterizing and auditing training data for improved regression [80.11415390605215]
  We introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings.
  arXiv  Detail & Related papers  (2023-10-29T10:31:59Z)
• ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks [18.66289473659838]
  We propose an innovative deep learning approach for imputing missing data. A graph attention architecture is employed to capture the spatial correlations present in traffic data. A bidirectional neural network is utilized to learn temporal information.
  arXiv  Detail & Related papers  (2023-05-10T22:15:40Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns [3.9318191265352196]
  We propose a novel deep learning framework called Dynamic Spatio Graph Contemporal Networks (DSTG) to impute missing traffic data. We introduce a graph structure estimation technique to model the dynamic spatial dependencies real-time traffic information and road network structure. Our proposed model outperforms existing deep learning models in all kinds of missing scenarios and the graph structure estimation technique contributes to the model performance.
  arXiv  Detail & Related papers  (2021-09-17T05:47:17Z)
• OR-Net: Pointwise Relational Inference for Data Completion under Partial Observation [51.083573770706636]
  This work uses relational inference to fill in the incomplete data. We propose Omni-Relational Network (OR-Net) to model the pointwise relativity in two aspects.
  arXiv  Detail & Related papers  (2021-05-02T06:05:54Z)
• Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff [66.59869239999459]
  In finance, imputation of missing returns may be applied prior to training a portfolio optimization model. There is an inherent trade-off between the look-ahead-bias of using the full data set for imputation and the larger variance in the imputation from using only the training data. We propose a Bayesian posterior consensus distribution which optimally controls the variance and look-ahead-bias trade-off in the imputation.
  arXiv  Detail & Related papers  (2021-02-25T09:05:35Z)
• Spatio-Temporal Functional Neural Networks [11.73856529960872]
  We propose two novel extensions of the Neural Functional Network (FNN), a temporal regression model whose effectiveness has been proven by many researchers. The proposed models are then deployed to solve a practical and challenging precipitation prediction problem in the meteorology field.
  arXiv  Detail & Related papers  (2020-09-11T21:32:35Z)
• Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images [7.053891669775769]
  We have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TGAN) In this study, we have developed a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset.
  arXiv  Detail & Related papers  (2020-05-05T19:14:02Z)
• A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation [13.48205738743634]
  Missing data imputation is common in intemporal traffic data collected from various sensing systems. We present an efficient algorithm to obtain the optimal solution for each variable. The proposed model also outperforms other baseline models in extreme missing scenarios.
  arXiv  Detail & Related papers  (2020-03-23T13:27:01Z)

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.