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Queuing network analysis for waterways with artificial neural networks

Published online by Cambridge University Press:  01 November 1999

LIANG ZHU
Affiliation:
Center for Advanced Transportation Studies, University of Maryland, College Park, Maryland 20742, U.S.A.
PAUL SCHONFELD
Affiliation:
Center for Advanced Transportation Studies, University of Maryland, College Park, Maryland 20742, U.S.A.
YEON MYUNG KIM
Affiliation:
Korea Transport Institute, Seoul, Republic of Korea
IAN FLOOD
Affiliation:
M.E. Rinker School of Building and Construction, University of Florida, Gainesville, Florida
CHING-JUNG TING
Affiliation:
Yuan Ze University, Taoyvan, Taiwan, Republic of China

Abstract

An Artificial Neural Network (ANN) model has been developed for analyzing traffic in an inland waterway network. The main purpose of this paper is to determine how well such a relatively fast model for analyzing a queuing network could substitute for far more expensive simulation. Its substitutability for simulation is judged by relative discrepancies in predicting tow delays between the ANN and simulation models. This model is developed by integrating five distinct ANN submodels that predict tow headway variances at (1) merge points, (2) branching (i.e., diverging) points, (3) lock exits, and (4) link outflow points (e.g., at ports, junctions, or lock entrances), plus (5) tow queuing delays at locks. Preliminary results are shown for those five submodels and for the integrated network analysis model. Eventually, such a network analyzer should be useful for designing, selecting, sequencing, and scheduling lock improvement projects, for controlling lock operations, for system maintenance planning, and for other applications where many combinations of network characteristics must be evaluated. More generally, this method of decomposing complex queuing networks into elements that can be analyzed with ANNs and then recombined provides a promising approach for analyzing other queuing networks (e.g., in transportation, communication, computing, and production systems).

Type
Research Article
Copyright
© 1999 Cambridge University Press

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