This paper investigated the feasibility and reliability of employing various physiological measures -for determining drivers’ fatigue levels, which may ultimately lead to a solution for real-time detection of driver fatigue state for improving driving and traffic safety. An experimental study was conducted to collect the data, including fatigue levels assessed via the Karolinska sleepiness scale and heart rate variability (HRV) and electrodermal activity (EDA) features. Based on an extensive statistical analysis of the collected data, significant differences in numerous HRV and EDA features were found across varying fatigue levels. Employing several machine learning techniques for classification purposes, the most favorable binary classification performance was achieved using the Light Gradient Boosting Machine classifier, with an accuracy rate of 88.7% when HRV and EDA features were utilized as inputs. Meanwhile, for three-class classification, the accuracy decreased slightly to 85.6% when employing the Random Forest classifier. These outcomes underscore the potential of HRV and EDA feature fusion in capturing diverse physiological responses to fatigue, thereby bolstering fatigue detection performance. Besides, subject-independent classification yielded an accuracy of 52.0% and 53.3%, reflecting the potential bias introduced by unobserved heterogeneity in classification models. Moreover, feature selection should be prioritized over dimensionality reduction in feature fusion endeavors to diminish feature redundancy and prevent information loss. The findings of this study could contribute to the development of reliable driver fatigue detection methodologies utilizing readily available measures of physiological response measures, such as HRV and EDA features.

Pilots’ excessive mental workload could reduce their ability to perform concurrent tasks during emergency flights, which is one of the most critical aviation safety concerns. Several past efforts have attempted to investigate the underlying issues, but all had limited success owing to the challenge of collecting representative data under realistic operating conditions. This study aimed to address this challenge by conducting a flight simulator study involving a comparatively large number of participants, who were pilot cadets with flight experience, and using noninvasive functional near-infrared spectroscopy (fNIRS) to collect the pilots’ brain activity data. Pilots’ subjective ratings and brain activity records were collected over a total of 75 simulated flights under three subtask scenarios comprising different equipment failures. A statistical analysis was carried out on the subjective ratings and on the changes observed in the saturation of the oxyhemoglobin (ΔOxyHb) of individual fNIRS channels. The mental workload of the pilots was classified using a support vector machine hierarchical combination classifier, focusing on the question of whether it is feasible to classify pilots’ mental workload using brain activity signals (i.e., ΔOxyHb). The results suggested that the pilots’ mental workload levels were highly associated with the ΔOxyHb measures as well as with the activities of different brain regions, including the prefrontal-, motor-, and occipital cortex. The findings from this study could provide a reference for optimizing pilot training systems and improving pilot performance during emergency flight operations.

The stress level of high-speed rail (HSR) train drivers directly impacts their job performance and thus the safety of HSR operations. This paper attempts to develop a quantitative understanding of train drivers’ stress levels and the contributing factors by the experimental study conducted in a realistic HSR simulator. An extensive statistical analysis found that the ultra-short-term heart rate variability metrics could differentiate different stress levels. Three different machine-learning classifiers were evaluated for stress detection, including support vector machine (SVM), random forests (RF), and K-nearest neighbour (KNN). The RF model was shown to perform the best in terms of robustness and classification accuracy. Moreover, the research found that the driver’s stress level should be detected rather than the type of stressor. The findings from this research could contribute to the development of real-time HSR driver condition monitoring systems and the improvement of current HSR operation safety regulations.

Driver fatigue has a direct impact on urban railway transit (URT) drivers’ driving behavior and can cause driver error. The existing methods for fatigue detection mainly train the models with supervised learning, relying heavily on the annotation of recorded data. However, labeled data are unobtainable in some environments, especially for URT driver fatigue levels during actual driving. Therefore, this study proposes a fatigue detection method using unlabeled heart rate variability data to monitor URT driver fatigue in actual working conditions. By utilizing the existing conclusions with regard to factors contributing to fatigue and physiological changes, this study annotated a small number of samples and then used a novel positive and unlabeled learning algorithm based on nearest neighbors and random forest to divide samples into different fatigue levels. The proposed method was evaluated using the URT driver fatigue data sets collected in the lab. Binary classification achieved an accuracy of 79.0%. However, the accuracy of three-class classification was only 55.7%. In addition, the proposed method performed as well using the field data set as it did using the lab data set. The results show the high generalization performance of the proposed method, which could contribute to addressing the issue of lack of labeled training data for fatigue detection in actual working conditions.

Understanding the driving state (e.g., fatigue state) is critical in improving the safety of urban rail transit operations. This study reports a study that used simulation experiments to evaluate the effects of fatigue on professional drivers’ psychological, biological, and physical responses and thus develop a fatigue detection model, enabling early warning and ensuring operational safety. Specifically, we applied statistical analytics on self-reported fatigue feelings, the Mackworth Clock Task, the Stroop Test, and the Iowa Gambling Task to evaluate the impact of fatigue on driver performance. The results show that fatigue reduced participants’ reaction time and cognitive ability and led professional drivers to make more conservative decisions. Moreover, we utilized heart rate signals, electrodermal activity, and eye movements from wearable devices and cameras to build a driver fatigue detection model with machine-learning methods. We analyzed the impact of different time window lengths on fatigue detection. The results indicate that as the length of the time window increases, the data captures more comprehensive information, leading to improved fatigue detection accuracy. Furthermore, multi-feature fusion significantly enhanced model performance. With feature fusion, even short-term time windows can encapsulate sufficient information for effective fatigue detection. These findings not only contribute to a deeper understanding of the risks posed by fatigue in professional driving, but also pave the way to developing a real-time, non-invasive fatigue detection system, enhancing traffic safety.

Identification of causal factors in traffic crashes has always been a significant challenge in road safety studies. Traditional crash prediction models are limited in elucidating the underlying causal mechanisms in road crashes. This research explores the application of three graphic models, namely, the Gaussian graphical model (GGM), causal Bayesian network (CBN) and graphic extreme gradient boosting (XGBoost), through a case study using highway–railroad-grade crossing (HRGC) inventory and collision data from Canada. The three modelling approaches have generally yielded consistent findings on various risk factors such as crossing control type, track angle, and exposure, showing their potential for identifying causal relationships through the interpretation of causal graphs. With the ability to make better causal inferences from crash data, the effectiveness of safety countermeasures could be more accurately and reliably estimated.