• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1306-1312
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• 2011

Underground Subway station's air pollutants are introduced from the indoor or outdoor. And Radon is a major pollutant in the subway station. Radioactive substances Radon is occuring naturally in granite tunnel wall and underground water. Especially inert gas Radon that causes lung cancer in human is anywhere but 5678 S.M.R.T. tunnels deep and pass through the granite plaque have a lot of Radon. The Radon concentration is determined by the following reasons : radon content of soil and concrete, underground water, ventilation, pressure difference, building structure, temperature, etc. So Radon concentration is hard to predict. And we can't only ventilate owing to era of high oil prices. This study focuses on our efforts for the reduction of Radon concentration. And the purpose is to provide basically datas of specially managed 15 subway station's Radon concentration.

• Asian Journal of Atmospheric Environment
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• v.2 no.1
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• pp.60-67
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• 2008

The spatial limitations of urban environments in general lead to invention and design of a wide range of underground transportation systems such as subways, underground roads and paths, etc. Among them, the application of subway systems in metropolitan cities is most commonly observed to ease those confronted difficulties on this purpose. It in turn leaves passengers and workers to be exposed to indoor air potentially polluted by various sources existing in this underground environment. Specifically when considering the IAQ in a subway station, there exist many IAQ-related parameters to be counted either as individual or as integrated exposures. In this study, a model system has been developed to manage the general IAQ in a subway station. Field survey and $CO_2$ measurements were initially conducted to analyze and understand the relationship between the indoor and outdoor air quality while considering the internal pollution sources such as passengers, subway trains, etc. The measurement data were then employed for the model development with other static information. For the model development, the algorithm of simple continuity was built and applied to model the subway IAQ concerned. In this paper, the recent updated draft version of model developed will be reported and demonstrated.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.185-194
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• 2019

Objective: This study was conducted to investigate the intensity of the extremely low frequency magnetic fields(ELF-MF) generated inside of the cabins during subway operation. Methods: The ELF-MF intensity were investigated on 30 subway lines in Korea, including in the Greater Seoul Metropolitan Area(Seoul and Gyeonggi-do Province), Incheon, Busan, Daegu, Daejeon, and Gwangju. ELF-MF intensity was measured at 0.9 m from the floor using EMDEX II meters with a resolution of $0.01{\mu}T$. All data were collected every three seconds and analyzed with EMCALC 2013 version 3.0B software. Basic characteristics of subway operation, including alternative current(AC) or direct current(DC), voltage level, and opening year of the line were investigated. Real-time information during measurement, such as the time of departure, moving and arrival of trains, were also recorded. Results: The arithmetic mean(AM) and maximum(Max) intensity of ELF-MF were $0.62{\mu}T$ and $11.51{\mu}T$, respectively. Compared by region, the ELF-MF intensity measured inside cabin were the highest in the Seoul Metropolitan Area($AM=0.80{\mu}T$), followed by Busan($AM=0.30{\mu}T$), Daegu($AM=0.29{\mu}T$), Incheon($AM=0.14{\mu}T$), Gwangju($AM=0.04{\mu}T$) and Daejeon($AM=0.03{\mu}T$). The average ELF-MF level measured in AC trains($AM=1.36{\mu}T$) was also significantly higher than in DC trains($AM=0.28{\mu}T$). In terms of the opening year of the subway, trains opened before 1990($AM=0.85{\mu}T$) was the highest and the lowest was 2000-2009($AM=0.24{\mu}T$). Conclusions: The AC supply has the greatest influence on the generation of the ELF-MF intensity in subway cabins.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.613-621
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• 2019

In general, transfer in subway stations is defined as transfer between lines and station transfer. In transfer between lines, passengers change from one subway line to another by utilizing horizontal pedestrian facilities such as transfer passages and pedestrian way. Station transfer appears in the situation that subway lines of enter and exit gate terminals differs from those of boarding and alighting trains and passenger trips utilize both vertical pedestrian facilities such as stair and escalator and horizontal facilities. The hypothesis on these two transfers presupposes that all subway lines are operated by either local train or express in subway network. This means that in a transfer case both local and express trains are operated in the same subway line, as a case of Seoul Metro Line 9, has not been studied. This research proposes a methodology of finding the same line transfer in the Seoul metropolitan subway network built based on the smart card network data by suggesting expanded network concept and a model that passengers choose a theirs minimum time routes.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.187-205
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• 2009

Failures on network components of a public transportation system can give rise to the severe degradation of entire system functionality. This paper aims at exploring how potential failures can affect the system flows and reliability of subway network systems in Metropolitan Seoul. To evaluate the range of impacts of disruptions, this research employs a probabilistic approach, network reliability. Network reliability measures the network resiliency and probability of flow loss under a variety of simulated disruptions of critical network components, transfer stations in subway system. By identifying the best and worst scenarios associated with geographical impact, as well as evaluating the criticality of transfer stations, this research presents some insights for protecting current subways systems.

• Journal of KIBIM
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• v.9 no.4
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• pp.75-83
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• 2019

Recent data development has made it possible to analyze each individual's daily commuting by using transportation card transaction. This research utilizes about 1 million observations from the subway line no.7 of Seoul metropolitan transportation data. By using such a massive dataset, the authors try to identify daily travel behavior of morning commute and its possible relationship between subway usage and socio-economic factors. There are 4 main types of users and their travel behavior, and top 15 stations with the most users for arrival and departure are selected. Accordingly, 15 stations have distinctive characteristics including population density and the number of businesses around stations. To identify this fact, the 4 most populated stations are selected and their socio-economic factors are examined. According to the analysis, the most departure stations are generally surrounded by hihgly populated residential areas, whereas the most arrival stations are stood within the job concentrated districts.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.66-71
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• 2008

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.79-80
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• 2007

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.692-694
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• 2008

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.211-222
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• 2013

Discovering zones which a1re sets of geographically adjacent regions are essential in sophisticated urban developments and people's movement improvements. While there are some studies that separately focus on movements between particular regions and zone discovery, they show limitations to understand people's movements from a wider viewpoint. Therefore, in this research, we propose a clustering based analysis method that aims at discovering movement patterns, which involves zones and their relations, based on a big data of smart card transaction systems. Moreover, the effectiveness of discovered movement patterns is quantitatively evaluated by using the proposed metrics. By using a real-world dataset obtained in Seoul metropolitan subway networks, we investigate and visualize hidden movement patterns in Seoul.