Tapkin S., Akyılmaz O.

10th International Conference of Hong-Kong-Society-for-Transportation-Studies, Hong Kong, PEOPLES R CHINA, 10 December 2005, pp.288-297 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: Hong Kong
  • Country: PEOPLES R CHINA
  • Page Numbers: pp.288-297
  • Keywords: Trip Distribution, Gravity Model, Back-Propagation Artificial Neural Networks, Neural Trip Distribution Model, Modular Neural Network
  • Anadolu University Affiliated: Yes


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.