• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.201-210
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• 2019

Recently, due to the efforts to mitigate traffic congestion and expansion of space efficiency, the construction of underground roads has been increased in big-scale cities. Since tunnels in the city have a higher chance for a fire leading to a great tragedy during a severe traffic jam than mountain tunnels, it is highly likely that it will be constructed as a tunnel, having a small cross section, for small vehicles. However, if they are constructed as such small-vehicle tunnels, it would be possible to reduce the design fire intensity while the concentration of harmful gases would increase due to a reduction in the small cross sectional area, led by a decrease in the tunnel height. In this study, behaviors of fire smoke by the installation interval and format of large-scale exhaust-gas ports were examined and compared in the analysis of temperatures and CO concentrations of a tunnel and its results were as the following. Although there were no significant differences in the smoke spreading distance between installation intervals, but in this study, 100 m was found to be the most effective installation interval. The smoke exhaustion performance was found to be excellent in the order of $4m{\times}3m$, $6m{\times}2m$, and $3m{\times}2m$ (2 lane) of the smoke spreading distance. Although there was no significant difference in the smoke spreading distance between formats of large-scale exhaust-gas ports, it was found that the smoke spreading distance was larger than other cases when it was $3m{\times}2m$ in the fire growing process. The analysis of smoke spreading distances by the aspect ratio showed that a smoke spreading distance was shorted when its the smoke spreading distance was found to be shorter when its traverse distance was relatively longer than its longitudinal distance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.200-209
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• 2018

This study proposed a method to select existing facilities as a landmark in order to reset accumulated errors of dead reckoning in a tunnel difficult to receive GNSS signals in automated driving. First, related standards and regulations were reviewed in order to survey 'variety' on shapes and installation locations as a feature of facilities. Second, 'recognition' on facilities was examined using image and Lidar sensors. Last, 'regularity' in terms of installation locations and intervals was surveyed through related references. The results of this study selected a fire fighting box / lamp (50m), an evacuation corridor lamp (300m), a lane control system (500m), a maximum / minimum speed limit sign and a jet fan as a candidate landmark to reset positioning errors. Based on those facilities, it was determined that error correction was possible. The results of this study are expected to be used in repositioning of automated driving vehicles in a tunnel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.137-154
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• 2019

This study analysed the center control emergency vehicle preemption[EVP] test result on the 1.782 km section around Gangbuk Fire Station. The pros and cons between center control and site control EVP was compared through the review of existing research. The test site was selected based on the higher link speed for choosing low congested area and 4 to 6 lane road. EVP operates green extension under the estimated arrival time to each intersection. This study is about EVP system field application and its evaluation by analyzing EVP operation result with the emergency vehicle's trace, GPS data. The impact on the surrounding traffic was analysed in delay from the queue length survey. Analysis showed the decrease in averge travel time 41.81%, but the increase in delay of surrounding traffic slightly. It is expected that EVP can be applied to the expanded area by researching EVP compensation scheme.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.129-137
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• 2013

This study This study aims to identify sound developmental directions for transit-oriented development (TOD), which is a topic of interest in the field of urban development, and conducts an empirical analysis of station influence areas within Seoul according to the characteristics of transit types. The results suggest that there are differences among variables that influence demand for public transportation according to transit types. This means that it is necessary to identify improvement plans in developing public transport strategies for the capital region based on such differentiated influence factors and to build foundations to continue to increase demand for public transportation. Also, station influence areas should be developed into hubs by establishing appropriate bus-only lane networks as a supplementary means, rather than directly connecting subway stations with each other.

• Journal of the Korean Institute of Rural Architecture
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• v.17 no.3
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• pp.11-18
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• 2015

This study is aimed to clarify the transitional characteristics of old roads and alleys lasting more than 100 years(1915-2015) in traditional urban area(Seongan-dong) of cheongju, historic inland and castle city of Korea. Cheongju castle had been completely destroyed In 1915, and urban structures been also altered last 100 years from 2015 now. Periodically, after destruction of castle, existing roads were extended and transformed to straight line for connecting with around. Expanding urban area to all directions of castle boundary in 1930-40s, town planning were created. These projects were completed in 1960-70s, the street network was built as they are. Since the 1980s, changes had been occurred in the details such as an extension of the unexecuted roads, the opening of fire lane in a block, and etc. In change and construction of roads, urban district plan in 1939 and reorganization since 1967 were planned and established with the type, location, and width of the large, medium and small roads based on data before destruction of castle. Except the width of 25m Sajikro(large3-1) and Sangdangro(large3-8,9,10), the width of 15m Namsaro(medium2-1) and Namjuro (medium2-4) as an extension of the roads, the other roads were small roads equivalent to the existing roads, and so remain intact figure of streetscape. As such, roads of east-west and north-south cross type, roads showing the outline of Cheongju castle fortress, and alleys outside the south gate are sustained in Seongan-dong of cheongju as historic urban area, and also present roads are delicately executed to the existing urban fabric.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.