Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model

@article{Li2020EstimationOM,
  title={Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model},
  author={Linchao Li and Bowen Du and Yonggang Wang and Lingqiao Qin and Huachun Tan},
  journal={Knowl. Based Syst.},
  year={2020},
  volume={194},
  pages={105592}
}
Abstract With the development of sensing technology, a large amount of heterogeneous traffic data can be collected. However, the raw data often contain corrupted or missing values, which need to be imputed to aid traffic condition monitoring and the assessment of the system performance. Several existing studies have reported imputation models used to impute the missing values, and most of these models aimed to capture the spatial or temporal dependencies. However, the dependencies of the… Expand
Missing data imputation for traffic congestion data based on joint matrix factorization
Generative Imputation and Stochastic Prediction
Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
Traffic State Estimation of Bus Line With Sparse Sampled Data
Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

References

SHOWING 1-10 OF 41 REFERENCES
Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
...
1
2
3
4
5
...