• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.145-152
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• 1998

Driving performance is characterized by many things such as driver's experience period, age, ability of information processing and reaction time of control devices and so forth. However, each factor of driving performance is needed to help and screen a poor driver for safe driving. In this paper, driving performance was estimated by reaction of manipulating brake, accelerator, steering wheel and speed. Subjects were grouped by experience of accident and age. Combinations of every group were analysed. For all the dependent variables, only steering wheel and speed were shown to have significant difference, which could be regarded as visual information of speed and direction were the important factors to drive safely. Especially for tile elderly, it is needed to enhance their ability of visual information processing that is to be decreased with aging. Therefore driving simulator to train and screen the poor driver should be studied.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1486-1493
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• 2008

In this paper, we performed analysis and comparison on emergency response action for passengers, drivers, workers and controllers in case of failure occurrence on rail infrastructure and rolling stock. In general, the subjects of emergency response action perform the response action with following emergency response procedures when accidents occurred. In reality, however, no matter how well the subjects are trained, it is hard to follow the emergency response procedures precisely without making any mistakes. As for emergency response action, the most significant factor for the subjects is to follow the emergency response procedures as learned, without any hesitation. In this paper, therefore, we analyzed the emergency response actions that should be performed by passengers and railway workers when emergency accidents occurred. We also examined the communication facilities for emergency response among train, wayside and station in order to provide the emergency reporting system for passenger and the method for cleaning out the accident area.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Safety and Health at Work
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• v.10 no.3
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• pp.377-383
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• 2019

Objectives: Exposure to fine particles in urban air has been associated with a number of negative health effects. High levels of fine particles have been detected at underground stations in big cities. We investigated the exposure conditions in four occupational groups in the Stockholm underground train system to identify high-exposed groups and study variations in exposure. Methods: $PM_1$ and $PM_{2.5}$ were measured during three full work shifts on 44 underground workers. Fluctuations in exposure were monitored by a real-time particle monitoring instrument, pDR, DataRAM. Qualitative analysis of particle content was performed using inductively coupled plasma mass spectrometry. Nitrogen dioxide was measured using passive monitors. Results: For all underground workers, the geometric mean (GM) of $PM_1$ was $18{\mu}g/m^3$ and of $PM_{2.5}$ was $37{\mu}g/m^3$. The particle exposure was highest for cleaners/platform workers, and the GM of $PM_1$ was $31.6{\mu}g/m^3$ [geometric standard deviation (GSD), 1.6] and of $PM_{2.5}$ was $76.5{\mu}g/m^3$ (GSD, 1.3); the particle exposure was lowest for ticket sellers, and the GM of $PM_1$ was $4.9{\mu}g/m^3$ (GSD, 2.1) and of $PM_{2.5}$ was $9.3{\mu}g/m^3$ (GSD, 1.5). The $PM_1$ and $PM_{2.5}$ levels were five times higher in the underground system than at the street level, and the particles in the underground had high iron content. The train driver's nitrogen dioxide exposure level was $64.1{\mu}g/m^3$ (GSD, 1.5). Conclusions: Cleaners and other platform workers were statistically significantly more exposed to particles than train drivers or ticket sellers. Particle concentrations ($PM_{2.5}$) in the Stockholm underground system were within the same range as in the New York underground system but were much lower than in several older underground systems around the world.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1229-1234
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• 2009

The rolling stocks of KORAIL are KTX, Saemaulho Multiple Unit(PP), New Electrical Locomotive(DL), Electrical Locomotive(EL), Diesel Locomotive, Metropolitan Commuter Train(CDC), VVVF and Resistance Controlled Multiple Unit, etc. EMU with the maximum speed of 150km/h is under the test run at the moment. Electrical Multiple Units for mainlines with 180km/h speed are supposed to be introduced as a substitute for Saemaulho Multiple Unit which is scheduled to be out of service. But the specification standard for the control board design of train driver's cab does not exist and there is no a study for layout and type of controlling device with driver's ergonomical approach. That's why the types of controller and operating are different from rolling stocks, which has high possibility of driver's human error and needs education whenever a new car comes in. Based on the opinion poll of drivers, design specification of safety engineering and ergonomics for controlling devices and safety facilities can improve exact control for devices and deal quickly with an emergency so as to improve rolling stock safety and operational efficiency.

• Journal of the Korean Society for Railway
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• v.16 no.4
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• pp.241-245
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• 2013

In the case of the existing train control system, the driver monitors the condition of the vehicle through a composite controller device that displays various information on a screen in the vehicle. However, when problems arise such as car trouble, it is difficult for the drivers to take action immediately. In addition, maintenance personnel have to manually save data one by one after storing the vehicle to analyze control information of the main devices such as the brake controller and auxiliary power. To improve these points, a system that sends and receives all information in real time should be established by installing a sensor communication network and a surveillance system. This study attempts to improve the safety and maintenance of rail vehicles by suggesting a standardized method for train control and surveillance system.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.81-88
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• 2013

In Korean society, the theme of human errors in railway has been emerging as a critical issue. As far as human error studies are concerned, main trend has been inclined to be led by industrial engineering and systems science. Apart from those trends, this study empirically highlighted the relationship between depression, which has been a frequent research subject in the medical science and psychology, and accidents, with setting depression as an exogenous variable and cognitive failure and mistake as endogenous variables, respectively. Results of hypotheses test for the 204 respondents showed that driver's depression has a significant effect on accidents mediated by cognitive failure and mistake. This findings suggest the need for exploring the diverse latent factors causing human errors and for understanding the complex cognitive process as well as for establishing integrative countermeasures to mitigate human errors.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.2
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• pp.151-157
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• 2006

The passenger car drivers want in general to feel good driveability, but they sometimes feel uncomfortable by shock and jerk phenomena when they push or release acceleration pedal with clutch on state. In this paper, the shock and jerk characteristics are studied on the vehicles controlled by the throttle-by-wire system. Experiments and simulations were carried out on two vehicles which show different control characteristics. The engine torque control characteristics was analyzed by measuring cylinder pressure. Various specification factors of the vehicles and the torque control logic of the engines were simulated through experimental data basis. The result shows the spring effect of the trans-axle in the drive-train is one of the most important factors of the shock-jerk phenomena and the engine torque control method is also responsible for the reducing the shock-jerk amplitude. In this paper a new control logic of the engine torque is suggested for the better driveablility on the tip-in/out event.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.401-410
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• 2018

Human error of the engineer are constantly occurring by insufficiency of scientific analysis considering the behavior of railway engagements and actual working conditions and lack of research for accidents. This study investigated how to improve the system through AHP and FGI analysis of interviews and questionnaires for electric vehicles driving licenses, which cultivates the most train drivers.