Missing Data in Traffic Estimation: A Variational Autoencoder Imputation Method

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)


Road traffic forecasting systems are in scenarios where sensor or system failure occur. In those scenarios, it is known that missing values negatively affect estimation accuracy although it is being often underestimate in current deep neural network approaches. Our assumption is that traffic data can be generated from a latent space. Thus, we propose an online unsupervised data imputation method based on learning the data distribution using a variational autoencoder (VAE). This is used as an independent pre-processing step prior to traffic forecasting which is then evaluated against missing data of a real-world dataset. Compared to other methods, we show that VAE improves post-imputation traffic forecasting performance while allowing for data augmentation, data compression and traffic classification at the same time.

Original languageAmerican English
Pages (from-to)2882-2886
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - May 2019


  • deep learning
  • imputation method
  • intelligent transportation systems
  • missing data
  • traffic forecasting


Dive into the research topics of 'Missing Data in Traffic Estimation: A Variational Autoencoder Imputation Method'. Together they form a unique fingerprint.

Cite this