Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect Train driver drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting Train driver drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect drowsiness in real time, which can help prevent accidents.Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect Train driver drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting Train driver drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect drowsiness in real time, which can help prevent accidents.

Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect Train driver drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting Train driver drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect drowsiness in real time, which can help prevent accidents.Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect Train driver drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting Train driver drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect drowsiness in real time, which can help prevent accidents.

Background: Evidence suggests that Train drivers experience a high level of fatigue and mental workload. The present study aimed to assess overall, physical, and mental fatigue levels and their correlations with the mental workload in the metro Train operation. Study Design: A cross-sectional study. Methods: This study was conducted on all 1194 Train drivers in the Tehran Metro. The Train drivers completed the Samn-Perelli Fatigue Scale and the Fatigue Assessment Scales at the beginning and end of the shift. In addition, they completed the National Aeronautics and Space Administration Task Load Index in the middle and at the end of the shift. Correlation and regression analyses were performed on the data to test the study hypothesis. Results: Overall, physical, and mental fatigue levels increased significantly at the end of the shift compared to the onset of the shift (P<0. 001). The mental workload and related dimensions were significantly increased at the end of the shift compared to the middle of the shift (P<0. 001). Mental demand was the most important workload problem among the Train drivers. The highest correlation was found between overall workload and time pressure (R=0. 68, P<0. 001). Conclusion: The mental workload had a significant correlation with work fatigue in the Train drivers. Control measures should be focused on the mental workload and related dimensions, especially mental demand and time pressure. Work fatigue, Mental workload, Train driver, Metro