| dc.creator |
Kalyoncuoglu, SF |
|
| dc.creator |
Tigdemir, Mesut |
|
| dc.date |
2004-07-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:29:50Z |
|
| dc.date.available |
2020-10-06T09:29:50Z |
|
| dc.identifier |
1ead5156-97f0-42d1-bc55-85e99deae9d8 |
|
| dc.identifier |
10.1016/j.simpat.2004.04.002 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/1ead5156-97f0-42d1-bc55-85e99deae9d8/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/54945 |
|
| dc.description |
It is assumed that there is a complicated relationship between the driver characteristics and involvement in traffic accidents. It is quite difficult to simulate the effects of these driver characteristics into the traffic accidents. The artificial neural networks (ANN) approach is proposed for training-predicting the database in this paper since it is a more flexible and assumption-free methodology. The networks are organised in different architectures and the results have been compared in order to determine the best fitting one. Finally, the best possible architecture is selected for a better representation of the survey data and the prediction of accident percentage. The predictions about the outputs for the inputs which are not used in the training of the ANN provide information about the drivers which cannot be reached in the database. The predictions are highly satisfactory and the ANNs have been found to be reliable processing systems for modelling and simulation in the traffic data assessments. (C) 2004 Elsevier B.V. All rights reserved. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
An alternative approach for modelling and simulation of traffic data: artificial neural networks |
|
| dc.type |
info:eu-repo/semantics/article |
|