Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently.
Mining crowdsourced data on bicycle safety critical events
