In this dissertation, it is aimed to develop an approach for the trip distribution
element which is one of the important phases of four-step travel demand modelling.
The trip distribution problem using back-propagation artificial neural networks has
been researched in a limited number of studies and, in a critically evaluated study it
has been concluded that the artificial neural networks underperform when compared
to the traditional models. The underperformance of back-propagation artificial
neural networks appears to be due to the thresholding the linearly combined inputs
from the input layer in the hidden layer as well as thresholding the linearly combined
outputs from the hidden layer in the output layer. In the proposed neural trip
distribution model, it is attempted not to threshold the linearly combined outputs
from the hidden layer in the output layer. Thus, in this approach, linearly combined
iv
inputs are activated in the hidden layer as in most neural networks and the neuron in
the output layer is used as a summation unit in contrast to other neural networks.
When this developed neural trip distribution model is compared with various
approaches as modular, gravity and back-propagation neural models, it has been
found that reliable trip distribution predictions are obtained.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/3/663807/index.pdf |
| Date | 01 January 2004 |
| Creators | Tapkin, Serkan |
| Contributors | Akyilmaz, Ozdemir |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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