Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

By Ling Cai, Krzysztof Janowicz, Gengchen Mai, Bo Yan, Rui Zhu. Trans. GIS, 2020

372 citations recorded by the source corpus.

Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1ff858576af755583559fdfcd366bd16002da297

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

  1. Jiayun Luo
  2. Weiwei Jiang

In Transportation

  1. Jiayun Luo
  2. Weiwei Jiang

In Signal Processing

  1. Junchi Yan
  2. Ziqing Ma
  3. Weiwei Jiang
  4. Liang Sun
  5. Qingsong Wen
  6. Tian Zhou
  7. Qiang Xu
  8. Ailing Zeng

Citations in the corpus