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

Tomoyuki Okuda, Kouyu Suzuki, Naohiko Kohtake

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages824-830
Number of pages7
ISBN (Electronic)9781728103235
DOIs
Publication statusPublished - 2018 Dec 7
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States
Duration: 2018 Nov 42018 Nov 7

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume2018-November

Other

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

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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    Okuda, T., Suzuki, K., & Kohtake, N. (2018). Non-parametric Prediction Interval Estimate for Uncertainty Quantification of the Prediction of Road Pavement Deterioration. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018 (pp. 824-830). [8569337] (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2018.8569337