Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.

Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents.

Distracted driving is one of the main causes of road crashes. Therefore, effective distinguishing of distracted driving behaviour and its category is the key to reducing the incidence of road crashes. To identify distracted driving behaviour accurately and effectively, this paper uses the head posture as a relevant variable and realizes the classification of distracted driving behaviour based on the relevant literature and investigation. A distracted driving discrimination algorithm based on the facial feature triangle is proposed. In the proposed algorithm, the Bayesian network is employed to judge driving behaviour categories. The proposed algorithm is verified by experiments using data from 20 volunteers. The experimental results show that the discrimination accuracy of the proposed algorithm is as high as 90%, which indicates that the head posture parameters used in this study are closely related to the distracted driving state. The results show that the proposed algorithm achieves high accuracy in the discrimination and classification of distracted driving behaviour and can effectively reduce the accident rate caused by distracted driving. Moreover, it can provide a basis for the research of distracted driving behaviour and is conducive to the formulation of the corresponding laws and regulations.