Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks
Artificial Neural Networks (ANNs) were used in this study to estimate the hourly passenger populations at certain stations in İstanbul. To do this, the details were collected from various sources regarding the passengers in a station. This study aims to show what can be implemented for the passenger...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2019-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/325794 |
| _version_ | 1797207403370905600 |
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| author | Emrah Aydemir Sevinc Gulsecen |
| author_facet | Emrah Aydemir Sevinc Gulsecen |
| author_sort | Emrah Aydemir |
| collection | DOAJ |
| description | Artificial Neural Networks (ANNs) were used in this study to estimate the hourly passenger populations at certain stations in İstanbul. To do this, the details were collected from various sources regarding the passengers in a station. This study aims to show what can be implemented for the passenger numbers in the decision support system and makes some recommendations for the regulation of the bus lines. Trials were conducted using an ANN with a backpropagation model and various inner layers for the estimations. The MAE score was 10.301 for the stations studied. Qualitative interviews were conducted with 32 passengers and 12 drivers, and solutions were searched for the density of the lines. A proposal system was developed with the c# software resulting from the combination of the prediction model with these proposals. |
| first_indexed | 2024-04-24T09:22:21Z |
| format | Article |
| id | doaj.art-409d66cd93d64093ae70a4185de7812c |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:22:21Z |
| publishDate | 2019-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-409d66cd93d64093ae70a4185de7812c2024-04-15T15:41:24ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392019-01-0126488589210.17559/TV-20170629201111Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural NetworksEmrah Aydemir0Sevinc Gulsecen1Ahi Evran University, Yenice Mah. Terme Cad. No: 45 Merkez / Kirşehir, Turkeyİstanbul University, Kalenderhane Mah. 16 Mart Şehitleri Cad. Dr. Şevket Apt. No: 8 PK 34134 Vezneciler-Beyazıt-Fatih/İstanbul, TurkeyArtificial Neural Networks (ANNs) were used in this study to estimate the hourly passenger populations at certain stations in İstanbul. To do this, the details were collected from various sources regarding the passengers in a station. This study aims to show what can be implemented for the passenger numbers in the decision support system and makes some recommendations for the regulation of the bus lines. Trials were conducted using an ANN with a backpropagation model and various inner layers for the estimations. The MAE score was 10.301 for the stations studied. Qualitative interviews were conducted with 32 passengers and 12 drivers, and solutions were searched for the density of the lines. A proposal system was developed with the c# software resulting from the combination of the prediction model with these proposals.https://hrcak.srce.hr/file/325794Artificial Neural Networksestimationİstanbulpublic transportation |
| spellingShingle | Emrah Aydemir Sevinc Gulsecen Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks Tehnički Vjesnik Artificial Neural Networks estimation İstanbul public transportation |
| title | Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks |
| title_full | Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks |
| title_fullStr | Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks |
| title_full_unstemmed | Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks |
| title_short | Arranging Bus Behaviour by Finding the Best Prediction Model with Artificial Neural Networks |
| title_sort | arranging bus behaviour by finding the best prediction model with artificial neural networks |
| topic | Artificial Neural Networks estimation İstanbul public transportation |
| url | https://hrcak.srce.hr/file/325794 |
| work_keys_str_mv | AT emrahaydemir arrangingbusbehaviourbyfindingthebestpredictionmodelwithartificialneuralnetworks AT sevincgulsecen arrangingbusbehaviourbyfindingthebestpredictionmodelwithartificialneuralnetworks |