Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks
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 includ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad
2016-09-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | http://joe.uobaghdad.edu.iq/index.php/main/article/view/150 |
| _version_ | 1797715701355511808 |
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| author | Sawsan Rasheed Mohamed, Ass. Prof. Dr. Abbas Mohammed Mohammed Burhan, Lecturer Ahmed Mohammed Ali Hadi, Lecturer |
| author_facet | Sawsan Rasheed Mohamed, Ass. Prof. Dr. Abbas Mohammed Mohammed Burhan, Lecturer Ahmed Mohammed Ali Hadi, Lecturer |
| author_sort | Sawsan Rasheed Mohamed, Ass. Prof. Dr. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T08:10:34Z |
| format | Article |
| id | doaj.art-3575afec36024f4e9c9c0318324d3651 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-12T08:10:34Z |
| publishDate | 2016-09-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-3575afec36024f4e9c9c0318324d36512023-09-02T19:07:54ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392016-09-01229Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural NetworksSawsan Rasheed Mohamed, Ass. Prof. Dr.0Abbas Mohammed Mohammed Burhan, Lecturer1Ahmed Mohammed Ali Hadi, Lecturer2College of Engineering-University of BaghdadCollege of Engineering-University of Baghdadcollege of Engineering - Al-Mustansiriyah UniversityThe 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.http://joe.uobaghdad.edu.iq/index.php/main/article/view/150Indicators, Railway Lines, Transport Density Index, Neural Networks |
| spellingShingle | Sawsan Rasheed Mohamed, Ass. Prof. Dr. Abbas Mohammed Mohammed Burhan, Lecturer Ahmed Mohammed Ali Hadi, Lecturer Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks Journal of Engineering Indicators, Railway Lines, Transport Density Index, Neural Networks |
| title | Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks |
| title_full | Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks |
| title_fullStr | Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks |
| title_full_unstemmed | Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks |
| title_short | Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks |
| title_sort | calculating the transport density index from some of the productivity indicators for railway lines by using neural networks |
| topic | Indicators, Railway Lines, Transport Density Index, Neural Networks |
| url | http://joe.uobaghdad.edu.iq/index.php/main/article/view/150 |
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