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).
The calibration method of a tandem queueing model with PH service time using NS-3 simulation of a multihop wireless network