Institute of Transportation, MOTC

       This study extracts driving features from the vehicle data by the advanced driver assistance system (ADAS) on the bus ,and uses machine learning algorithms to construct a driving risk assessment model. The machine learning algorithms include unsupervised learning (clustering) to label risk levels by risk features, and the supervised learning (classification) that use driving behavior features to establish risk classification models. Also, using recursive feature elimination algorithm to identify key features. Moreover, the two topics of using the second-order machine learning architecture and distinguishing driving characteristics in the past research were discussed, and three risk assessment models were established to test necessity and model accuracy. As a case study, using Mobileye data from domestic highway bus carrier, which operates from Hsinchu to Taipei. The k-means clustering method is used to cluster the driving risk levels and then find out the key driving behavior features by RFE. The results show that the accuracy of the three models are higher than 90% and not much different. It means that the assessment of driving risk depends on the driving features used by the risk label. So, it is unnecessary to carry out the risk classification model after risk label, divided features into two types neither. Therefore, among three models, the one-order mixed feature model (Model-3) only performs machine learning once, which can better avoid the situation of being players and referees in the same time. Considering the time for data collection and processing and the time of model retraining in the future, the one-order mixed feature model is the better choice