• Journal of Korean Society for Atmospheric Environment
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• v.29 no.1
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• pp.74-85
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• 2013

Since the underground transportation system is a closed environment, indoor air quality problems may seriously affect many passengers' health. The purpose of this study was to understand $PM_{10}$ characteristics in the underground air environment and further to quantitatively estimate $PM_{10}$ source contributions in a Seoul Metropolitan subway station. The $PM_{10}$ was intensively collected on various filters with $PM_{10}$ aerosol samplers to obtain sufficient samples for its chemical analysis. Sampling was carried out in the M station on the Line-4 from April 21 to 28, July 13 to 21, and October 11 to 19 in the year of 2010 and January 11 to 17 in the year of 2011. The aerosol filter samples were then analyzed for metals, water soluble ions, and carbon components. The 29 chemical species (OC1, OC2, OC3, OC4, CC, PC, EC, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Si, Ti, V, Zn, $Cl^-$, $NO_3{^-}$, $SO_4{^{2-}}$, $Na^+$, $NH_4{^+}$, $K^+$, $Mg^{2+}$, $Ca^{2+}$) were analyzed by using ICP-AES, IC, and TOR after proper pretreatments of each sample filter. Based on the chemical information, positive matrix factorization (PMF) model was applied to identify the $PM_{10}$ sources and then six sources such as biomass burning, outdoor, vehicle, soil and road dust, secondary aerosol, ferrous, and brakewear related source were classified. The contributions rate of their sources in tunnel are 4.0%, 5.8%, 1.6%, 17.9%, 13.8% and 56.9% in order.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.1-17
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• 2018

The living lab is a user-participatory innovation space where users can solve problems by themselves. Living Lab members are able to participate in all aspects of product development from technology conception. In this study, to prevent pedestrian accidents, auto-image sensing signal system was developed in Jeonju City, using the Living Lab method. In addition, we measured effectiveness of the auto-image sensing signal system with respect to pedestrian waiting time, pedestrian and driver signal violation, and pedestrian jaywalking. It was also compared the measures before installation, after installation and after applying Living Lab method. As a result, all of the three measures of effectiveness appeared to be more effective after Living Lab than after installation. Overall, this study is very significant in that it is the first case where the living lab is applied in transportation.

• Tunnel and Underground Space
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• v.19 no.3
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• pp.236-247
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• 2009

Overpopulation has significantly increased the use of underground spaces in urban areas, and led to the developments of shallow-depth underground space. Due to unexpected rock fall, however, it is very necessary to understand and categorize the rock mass behaviors prior to the tunnel excavation, by which unnecessary casualties and economic loss could be prevented. In case of cave-in, special attention should be drawn since it occurs faster and greater in magnitude compared to rock fall and plastic deformation. Types of cave-in behavior are explained and categorized using seven parameters - Uniaxial Compressive Strength (UCS), Rock Quality Designation (RQD), joint surface condition, in-situ stress condition, ground water condition, earthquake & ground vibration, tunnel span. This study eventually introduces a new index called Cave-in Behavior Index (CBI) which explains the behavior of cave-in under given in-situ conditions expressed by the seven parameters. In order to assess the mutual interactions of the seven parameters and to evaluate the weighting factors for all the interactions, survey data of the experts' opinions and Rock Engineering Systems (RES) were used due to lack of field observations. CBI was applied to the tunnel site of Seoul Metro Line No. 9. UDEC analyses on 288 cases were done and occurrences of cave-in in every simulation were examined. Analyses on the results of 288 cases of simulations revealed that the average CBI for the cases when cave-in for different patterns of tunnel support was estimated by a logistic regression analysis.