• Proceedings of the KSR Conference
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• 2008.06a
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• pp.559-571
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• 2008

Platform Screen Door(PSD) has been installed and operated at seoul subway line $1\sim4$ in the manual train operation mode(ATS/ATC) by SeoulMetro since 2005. SeoulMetro uses the wireless (RF) communication system and the train door detection system for the link between the train and PSD doors opening/closing motion in the manual train operation mode. For the convenience and safety of passengers, the train doors and the PSD doors opening/closing shall be synchronized as much as possible. In ATO(Automatic Train Operation) mode which provides the interface between train control system and PSD system, ATO signaling system makes the train doors and PSD doors open/close command signals systematically, so PSD doors can be opened/closed almost simultaneously with the train doors. But, in the manual train control(ATS/ATC) mode, PSD system needs to detect the train doors open/close operation and make PSD open/close command signals to actuate PSD doors. These PSD open/close commanding process cause time delay of PSD doors opening/closing motion in response to the train doors opening/closing motion. Sometimes the response delay time can be over 1 second, which is not proper to operate PSD, and need to be reduced This paper presents the reduction method of the PSD response delay time to improve the convenience and safety of passengers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.1
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• pp.73-79
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• 2009

In this paper, the structural analysis and optimization of the door supporting rail structure in the header box located at the top of the aluminum passenger safety door(PSD) at the subway station, which opens and closes regularly, is performed. In case the simple fixed boundary condition is used for the bolt fixture on the supporting rail where the glass door is moving, excessive stresses are obtained. Therefore, more realistic finite element modeling of the bolts is used at the bolt fixture in the whole structure in order to obtain the more physically acceptable FEM results. As a result, fatigue life of twenty years of the structure is obtained to satisfy the design object. Also the optimal design of cross section of the rail part is performed using the response surface method and 15% of weight of the supporting rail part on the door is reduced.

• Journal of the Korean Society for Railway
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• v.11 no.1
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• pp.95-100
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• 2008

The simulation analysis of fire-driven flow and passenger evacuation in Daegu subway station, Chung-Ang, have been performed. The first location of outbreak of fire is inside passenger car in the third basement in Chung-Aug station, The smoke flow in the second and third basement has been analyzed using FLUENT 6.2. The CO (carbon monoxide) and temperature distribution in the train units and station platform have been obtained and transferred to input data for evacuation simulation. The highest temperature in the train units was 1500k. For the simulation of passenger evacuation, EXODUS has been used for whole basements (level 1${\sim}$level 3) in the station. Total number of people was assumed to be one thousand and 640 were placed inside train and 360 were placed outside train. In evacuation simulation, an average of 135 passengers were killed and an average time to evacuate takes 10min 19sec. The main evacuation routes used by passengers were investigated and the cause of death was identified by evacuation simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.95-104
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• 2012

Pattern recognition using ensemble classifiers is composed of strong classifier which consists of many weak classifiers. In this paper, we used feature extraction to organize strong classifier using static camera sequence. The strong classifier is made of weak classifiers which considers environmental factors. So the strong classifier overcomes environmental effect. Proposed method uses binary foreground image by frame difference method and the boosting is used to train crowdedness model and recognize crowdedness using features. Combination of weak classifiers makes strong ensemble classifier. The classifier could make use of potential features from the environment such as shadow and reflection. We tested the proposed system with road sequence and subway platform sequence which are included in "AVSS 2007" sequence. The result shows good accuracy and efficiency on complex environment.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1346-1353
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• 2007

The simulation analysis of fire-driven flow and passenger evacuation in Daegu subway station, Chung-Ang, have been performed. The first location of outbreak of fire is inside passenger car in the third basement in Chung-Ang station. The smoke flow in the second and third basement has been analyzed using FLUENT 6.2. The CO(carbon monoxide) and temperature distribution in the train units and station platform have been obtained and transferred to input data for evacuation simulation. The highest temperature in the train units was 1500K. For the simulation of passenger evacuation, EXODUS has been used for whole basements (level 1${\sim}$ level 3) in the station. Total number of people was assumed to be one thousand and 640 were placed inside train and 360 were placed outside train. In evacuation simulation, an average of 135 passengers were killed and an average time to evacuate takes 10min 19sec. The main evacuation routes used by passengers were investigated and the cause of death was identified by evacuation simulation.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.6
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• pp.692-702
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• 2011

For more effective indoor radon reduction policy and technique, we researched radon data analysis for some buildings in Seoul. Those buildings were categorized as dwelling, underground and office space and the variations of radon concentration and its sources were evaluated. The variations of radon concentrations of indoor space of buildings for a day were patterned specifically by dwelling habits and different environment. As for the new built apartments which were not yet moved in, their indoor radon concentrations were showed more than 3 times after applying interior assembly, and were 5 times higher than ones of rather old residences. As for the subway stations, the radon concentrations during off-run times were about 15% higher than run-times. 10% of radon seemed to be reduced by installation of platform screen doors. As for office space, radon concentrations during working hours were about 2.5 times higher than non-working hours. Plaster board are expected as a main source of radon for them. By radon measurement method for long-term, its data can be over estimated because it covers non-active time in office or public space. Therefore combination of short and long-term measurement method is required for effective and economic reduction. Furthermore importance of ventilation is requested as public information service for all dwelling space. And also standardization for radium content or radiation of radon is necessary.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.561-566
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• 2016

A subway platform is equipped with screen doors in oder to avoid accidents of passengers, where Area sensors are installed for detecting obstacles in the screen doors. However, there exist frequent operating errors in screen doors due to dusts, sunlight, snow, and bugs. It is required to develope a detection device which reduces errors and elaborates detection function. In this paper, we compared the detection rates of the Area sensor the 3D sensor using CCTV-based image data with installing sensors at the screen door in Munyang station Daegu, where 3D sensor is applied with the space division multiple detection algorithms. It is measured that the detection rate of 3D sensor and Area sensor is approximately 89.61% and 78.88%, respectively. The results confirmed that 3D senor has higher detection rate compared with Area sensor with the rate of 6.87~9.79%, and 3D sensor has benefit in the aspect of installation fee.

• Land and Housing Review
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• v.14 no.3
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• pp.77-92
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• 2023

The aim of the study is to analyze urban characteristics that affect carbon emissions of buildings. The analysis was conducted at the level of 424 administrative districts in Seoul. The main variables used in the analysis were energy consumption and carbon emissions of buildings published in the Seoul Metropolitan Government's energy information platform 2021. It was found that carbon emissions per unit building were high in Jongno, Gangnam, Guro, and Mok-dong. A regression analysis using the spatial lag model (SLM) identifies that the variables that affect the carbon emissions of buildings were; commercial, educational, business and industrial facility variables as built environment factor; number of residents; traffic volume, number of bus routes and number of subway stations as transportation facilities factors; and environmental factors such as green area and river area.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.