Non-parametric Prediction Interval Estimate for Uncertainty Quantification of the Prediction of Road Pavement Deterioration

Tomoyuki Okuda, Kouyu Suzuki, Naohiko Kohtake

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Road pavements need to be efficiently maintained under budget constraints. A pavement management system supports a road administrator's decision making based on the prediction of pavement deterioration. However, the prediction of pavement deterioration is complicated and uncertain because there are many unobservable variables, and the highly accurate prediction of deterioration is difficult. For pavement administrators to use such predictions in decision making, it is necessary to quantify the reliability of prediction. This paper proposes a prediction interval estimation method by applying the bootstrap method with a reduced computational cost to the deterioration prediction model using a neural network. The proposed method is applied to the rutting depth prediction in the inspection history of road pavement surface, and the estimation accuracy of the prediction interval is verified. In the prediction model, because the inspection history is time-series data, a recurrent neural network model that extends neural networks to time series prediction is used. Verification shows that not only is the computational cost reduced but also the accuracy of the prediction interval is higher than that of the conventional method.

本文言語English
ホスト出版物のタイトル2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ824-830
ページ数7
ISBN(電子版)9781728103235
DOI
出版ステータスPublished - 2018 12 7
イベント21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
継続期間: 2018 11 42018 11 7

出版物シリーズ

名前IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
2018-November

Other

Other21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
国/地域United States
CityMaui
Period18/11/418/11/7

ASJC Scopus subject areas

  • 自動車工学
  • 機械工学
  • コンピュータ サイエンスの応用

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