Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing... •A hybrid PSO-ELM model is improved for interval prediction of short-term traffic.•The reliability and sharpness of the prediction intervals are considered.•The improved PSO-ELM model is compared with five other models.•A comprehensive optimal staffing plan development framework is proposed.•Performances of staffing plans from prediction intervals and point predictions are compared. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Transportation Research Part C: Emerging Technologies Elsevier

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

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Publisher
Pergamon
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0968-090X
D.O.I.
10.1016/j.trc.2018.05.012
Publisher site
See Article on Publisher Site

Abstract

•A hybrid PSO-ELM model is improved for interval prediction of short-term traffic.•The reliability and sharpness of the prediction intervals are considered.•The improved PSO-ELM model is compared with five other models.•A comprehensive optimal staffing plan development framework is proposed.•Performances of staffing plans from prediction intervals and point predictions are compared.

Journal

Transportation Research Part C: Emerging TechnologiesElsevier

Published: Jul 1, 2018

References

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