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Affiliation
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Cukurova University, Department of Automotive Engineering , 01330 Adana , Turkey , Email: kdraydin@cu.edu.tr. |
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Abstract
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OBJECTIVE: Currently, in Turkey, fault rates in traffic accidents are determined according to initiative of accident experts (sometimes no speed analyses of vehicles, just procession of accident) and there are no specific quantitative instructions on fault rates related to procession of accident in No.2918 Turkish Highway Traffic Act (THTA). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage only (PDO) traffic accidents in Turkey.
METHODS: In this study, data (police reports, skid marks, deformation situation of involvements, crush depth, etc.) collected from the most-frequent and controversial accident types (four sample vehicle-vehicle scenarios), which consist of property damage only (PDO), were inserted into a reconstruction software called vCrash. Sample real world scenarios were simulated on the software to generate different deformations on vehicles which also correspond to energy equivalent speed (EES) data just before the crash. These values were used to train Multi-Layer Feedforward Artificial Neural Network (MFANN), Function Fitting Neural Network (FITNET) (a specialized version of MFANN) and Generalized Regression Neural Network (GRNN) models within 10-fold cross-validation to predict fault rates without necessity of any software. The performance of the Artificial Neural Network (ANN) prediction models was evaluated using Mean Square Error (MSE) and multiple correlation coefficient (R).
RESULTS: It was shown that MFANN model performs better results on predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2 and 3 whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first three scenarios. Since there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, GRNN model depicted worst results for prediction. The R values for prediction of fault rates were close to 1 for all folds and scenarios.
CONCLUSIONS: This study focuses on exhibiting new aspect and scientific approach for determining fault rates of involvements in most frequent PDO accidents occurring in Turkey by implying some deficiencies in THTA and without regarding to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian related accidents may be analyzed as future work by developing new scientific models.
Language: en |