| Summary |
Hsin-hsien Liu, 2006.07
Feng Chia University - Graduate Institute of Traffic and Transportation Engineering and Management
The roadway traffic accidents are increasing yearly, and the clients of the traffic accident want to protect their own rights, so that the cases of traffic accidents need to be authenticated are increasing simultaneously. However, the Local Traffic Authentication Committee (LTAAC) is lack of manpower, and the quoted authentication criteria are inconsistent by the different LTAAC. Therefore, it results a delay and decreasing the quality of the authentication case.
This study uses three methods such as the Procedural Authentication Method (PAM), the Classification Tree Method (CTM), and the Artificial Neural Network (ANN) to construct those authentication models, so that we can use these models to predict the responsibilities of the clients in a traffic accident. Also this study mainly focuses on the two-vehicle collision which doesn’t include pedestrian and bicyclist. The total data includes 2,634 cases and 5,268 clients. First, the PAM uses literature review and brainstorming to find the authentication criteria. Second, the CTM uses the cross table analysis to pick up the major factors as the input variables, and then sets up the different end-node numbers. Finally, the CTM produces 30 sub-models for validating. Third, the ANN method also uses the cross table analysis to pick up the major factors as the input variables, and sets up the different neuron numbers in the hidden layer. Finally, the ANN method also produces 30 sub-models. There are three collision types: car/car、car/motorcycle、and motorcycle /motorcycle. Both the CTM and the ANN methods will use 80 percentages of cases in database for training, and 20 percentages of cases for validating. This study shows that under the existing criteria, the PAM has the better results than the CTM and ANN method such as the accuracy percentage of the PAM are 74.1%, the accuracy percentage of the CTM are 71.92%, and the accuracy percentage of the ANN method are 67.17%. However, if we include the total client data, the accuracy percentage of the PAM will reduce into 62.5%. |
