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.

Introduction: Screening of risk factors for metabolic syndrome among commercial and Train drivers that are considered safety sensitive jobs, is an important issue in safety of transportation system. Metabolic syndrome is consisted of disturbed lipid profile, high blood pressure, and high waist circumference. It can lead to decrease the quality of life and higher health associated costs for these patients. This study aimed to assess risk factors for metabolic syndrome among Train drivers. Methods: This cross-sectional study was conducted on 281 Train drivers referred to their annual health examinations to Occupational Medicine Clinic of Baharloo Hospital, Tehran University of Medical Sciences. Demographic characteristics of participants were recorded. Blood pressure, body mass index and waist circumference of drivers were measured. Participants’ blood sample for fasting blood sugar, triglyceride, high and low density lipoproteins were collected. Metabolic syndrome was defined according to the NCEP ATP III criteria. Results: All of the participants were males. Their mean age and BMI was 43± 10 year and 26. 60± 3. 67 Kg/m 2, respectively. Fifty three (21%) of the participants were diagnosed with metabolic syndrome. The more frequent risk factor for metabolic syndrome was triglyceride more than 150mg/dl. Conclusion: Findings of the present study revealed high prevalence of metabolic syndrome among Train drivers. Further studies are needed for screening the metabolic syndrome in this occupational group as it is a sensitive occupation.

By developing the applications of intelligent information technology and systems, there is a possibility for learners to experience the process of learning and learning skills in virtual environment. Simulators; in virtual environments simulate normal and hazardous conditions such as accidents, rail accidents, and other disturbances in the rail network. The purpose of this study was to investigate the role of the use of educational simulator on the quality of Train path learner learning and to present some strategies for improving it in specialist Training centers of the internal rail transport industry. In the further study, the status of the Training simulator of the Metro Specialized Training Center has been evaluated in order to determine the usefulness of this tool in improving the quality. The research method was survey and the statistical population consisted of 254 teachers, Trainers, managers and learners of the specialized Training center for Train driving in Tehran metro. The sample size was 131 using Cochran's formula. The research data were collected using a researcher-made questionnaire and a closed response. Data were analyzed by chi-square test. The results of this study showed that the use of educational simulator has a positive effect on improving the learning quality of learners.

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

Considering the large amount of investment in railway transportation systems, effective use of resources is highly important. Typically, in order to utilize the line capacity, Train scheduling plays a substantial role and thus attracts lots of attention all over the world.Various Train-scheduling models are introduced, according to different conditions of different countries. However, for Iran, no comprehensive scheduling model has been proposed yet, due to obligatory stops for the passenger Trains to say their prayers.In this paper, for first time, a comprehensive mathematical model by considering the consTraint of obligatory stops for prayer is presented.The consTraint of obligatory stops for prayer is a domestic and religious attribute. In fact, Muslims believe it as essential to pray to God, in the certain times, with special rules or conditions. One of these rules is the restricted period of daily prayer. Another rule of praying is to keep still and calm, meanwhile. Moreover, the person must keep his/her body toward a certain direction, toward the City of Mecca, and should not turn away all during their prayers.All the rules and conditions, above, necessitate stopping of the Train in a certain period and in a suitable place. In this paper, the consTraint of praying is modeled in three steps:1. Recognition of the occurrence of praying conditions for each Train at each station.2. Recognition of stopping necessity for praying for each Train.3. Selection of the most appropriate station for stop.At step 1, in order to recognize whether a Train will reach a certain station in the permissible time of praying or not, it must confirm that the Train arrives at the station after the beginning of this period and before the end of it. Only in the existence of both these conditions, the Train can reach to the certain station in the permissible period and therefore can make a stop there.At step 2, it verifies that the Train has to stop for praying or not. In order to confirm the necessity or lack of necessity for stop at the intermediate station for praying, three groups of consTraints are designed and integrated in the model. The first group of consTraints verifies that the passengers have enough time for praying at the origin station. The second group of consTraints verifies that the passengers have enough time at the destination station. Finally, the third group of consTraints combines the results from the other two groups of consTraints and determines in which Trains, passengers do not have enough time for praying in either stations of origin and destination and therefore they must make a stop for praying on their way.At step 3, based on consequences from steps 1 and 2, the best station is determined for making a stop on their ways. Finally, a small example and 11 sample problems are solved and the results are presented.