Publication Cover
Transportation Letters
The International Journal of Transportation Research
Volume 14, 2022 - Issue 4
446
Views
5
CrossRef citations to date
0
Altmetric
Research Article

ST-FVGAN: filling series traffic missing values with generative adversarial network

ORCID Icon, , , &
 

ABSTRACT

The imputation of time series traffic flow data is of great significance to the intelligent transportation, urban planning, and road emergency handling.  This paper proposes a filling missing time series traffic data with Generative Adversarial Network (ST-FVGAN), which not only considers the spatio-temporal correlation and utilizes the idea of data generating of the Generative Adversarial Network, but also considers the external factors and introduces a more comprehensive loss function. Specifically, the model firstly constructs a Generator network which is composed of convolutional layer, residual block, and pixelshuffle block for the better potential distribution of the existing data, and then use the Discriminator network for the input judging. Experiments are conducted on the open-source TaxiBJ GPS dataset, and evaluated by the root mean square error (RMSE) index. The experimental results show that our model has the better accurate and reasonable performance than the traditional imputation methods

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the “Dynamic multi-objective requirement optimization based on transfer learning”,the National Science Foundation of China, Grant No. 61762092; “Code intelligent recommendation based on automatic neural architecture search under community evolution”, Key Laboratory in Software Engineering of Yunnan Province, Grant No. 2020SE303; “Genetic engineering of rare and precious metal materials in Yunnan Province”, Major projects of yunnan province, Grant No. 202002AB080001

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.