Transportation planners are often faced with the problem of estimating passenger and freight flows between regions. In the literature there are many models for passenger flows. However, models about freight flows are more limited. Modeling freight flow is also more complex than modeling passenger flow and there are many agents related with freight flows. In addition, data availability is a critical factor. In this research, freight flows between provinces in T&uuml / rkiye are forecasted by demand analysis. Transportation is one of the important activities of human beings and plays an important role for spatial interactions in economic growth. In other words, there is a very strong linkage between economic growth and the freight flow, thus transportation demand. Regional trade as spatial flow appears on transportation systems as freight flows. In this study, using the existing limited data and surveys in T&uuml / rkiye, nationwide origin-destination (O-D) matrix of freight flows between provinces is obtained. Using this empirical matrix, the generation of freight flows of provinces is formulated depending on the socioeconomic and demographic variables by means of multiple linear regression analysis. In addition, interactions of freight flows between provinces and economic growth of regions are investigated. The generations and attractions of provinces as freight flow are distributed between provinces with traditional gravity model. By comparing observed O-D matrix and simulated O-D matrix, gravity model is calibrated. Calibration is also performed by freight trip length distribution. In this research, two steps of traditional &ldquo / four-step analysis&rdquo / , &ldquo / trip generation&rdquo / and &ldquo / trip distribution&rdquo / , are applied to develop nationwide freight demand model between the provinces in T&uuml / rkiye. The developed model is single-mode, single commodity and nationwide.

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.