احتساب مؤشر كثافة النقل من بعض مؤشرات الإنتاجية لخطوط سكك الحديد باستخدام الشبكات العصبية

الملخص EN

The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives.

This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology.

Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted.

Training results and test to the neural network data used in the study, which are obtained from the international information network has showed that the error rate in the training and the testing process was about (10%) and that the results of the network query has given the results of acceptable accuracy statistically so that it was better than results obtained from multiple linear regression equation for the same data.