TY - GEN
T1 - A recommended neural trip distribution model
AU - Tapkin, Serkan
AU - Akyilmaz, Ozdemir
PY - 2005
Y1 - 2005
N2 - In this study, 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 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.
AB - In this study, 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 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.
KW - Back-propagation artificial neural networks
KW - Gravity model
KW - Modular neural network
KW - Neural trip distribution model
KW - Trip distribution
UR - http://www.scopus.com/inward/record.url?scp=84894596427&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84894596427
SN - 9889884712
SN - 9789889884710
T3 - Transportation and the Economy - Proceedings of the 10th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2005
SP - 288
EP - 297
BT - Transportation and the Economy - Proceedings of the 10th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2005
T2 - 10th International Conference of Hong Kong Society for Transportation Studies: Transportation and the Economy, HKSTS 2005
Y2 - 10 December 2005 through 10 December 2005
ER -