A spatio-temporal ensemble method for large-scale traffic state prediction

Yang Liu, Zhiyuan Liu, Hai L. Vu, Cheng Lyu

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

How to effectively ensemble multiple models while leveraging the spatio-temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio-temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio-temporal ensemble net. The proposed method is suitable for grid-based spatio-temporal prediction in dense urban areas. Experiments demonstrate that through spatio-temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.

Original languageEnglish
Pages (from-to)26-44
Number of pages19
JournalComputer-Aided Civil and Infrastructure Engineering
Volume35
Issue number1
DOIs
Publication statusPublished - Jan 2020

Cite this

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A spatio-temporal ensemble method for large-scale traffic state prediction. / Liu, Yang; Liu, Zhiyuan; Vu, Hai L.; Lyu, Cheng.

In: Computer-Aided Civil and Infrastructure Engineering, Vol. 35, No. 1, 01.2020, p. 26-44.

Research output: Contribution to journalArticleResearchpeer-review

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