00714941

Component

http://onlinepubs.trb.org...trr/1995/1497/1497-005.pdf

https://scholar.google.co...feld&publication_year=1995

Good service-time estimates at locks are essential for evaluating waterway performance, planning improvements, and controlling operations. Difficulties in estimation are due to great variations in lock characteristics, vessel characteristics, operating options, and environmental conditions. In this study several artificial neural network models for lock service-time estimation are developed and compared. Results show that simple artificial neural network models yield lower prediction errors than simple regression models, that systematic removal of outliers can reduce the number of artificial neural network prediction errors, and that combined service-time models for locks with dissimilar chambers can be obtained without unreasonably compromising accuracy.

This paper appears in Transportation Research Record No. 1497, Artificial Intelligence and Geographical Information. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved

01399824

English

p. 36-43

1995

Transportation Research Record

0309061636

Figures (7) ; References (7) ; Tables (7)

Accuracy; Errors; Estimating; Estimation theory; Locks (Waterways); Neural networks; Service time

Administration and Management; Highways; Marine Transportation; Planning and Forecasting

TRIS, TRB

Dec 20 1995 12:00AM

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