A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
A SVM-based cursive character recognizer
Pattern Recognition
Expert Systems with Applications: An International Journal
Automatic linguistic report of traffic evolution in roads
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The prediction of traffic accidents is one of most important issues in our life. In the prediction of traffic accidents, a GIS platform to extract the important features including day, temperature, humidity, weather conditions, and month of occurred traffic accidents has been used. In this study, a decision making system (DMS) based on correlation-based feature selection and classifier algorithms including support vector machine (SVM) and artificial neural network (ANN) has been proposed to predict the traffic accidents identifying risk factors connected to the environmental (climatological) conditions, which are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of geographical information systems (GIS). Locations of the motor vehicle accidents are determined by the dynamic segmentation process in ArcGIS 9.0 from the traffic accident reports recorded by District Traffic Agency. In this DMS, firstly the number of dimension of traffic accidents dataset with five features (ay, temperature, humidity, weather conditions, and month of occurred traffic accidents) has been reduced from 5 to 1 feature by using correlation-based feature selection (CFS). In CFS method, the correlation coefficients between five features and outputs (the cases of without accident or with accident) has been calculated and chosen the feature that has highest correlation coefficient. Secondly, the traffic accident cases with one feature have been classified as without accident or with accident using SVM and ANN models. The proposed DMS has obtained the prediction accuracy of 61.79% with ANN classifier and achieved the prediction accuracy of 67.42% using SVM with RBF (radial basis function) kernel. These results have indicated that the proposed DMS could be used on prediction of real traffic accidents.