• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.100-107
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• 2024

Currently, in Korea, due to the rapid aging and deterioration of facilities, the minimum Maintenance Level and Performance Level' of facilities are required by the 'Facility Safety Act' or 'Infrastructure Management Act'. Since infrastructure assets have a long lifespan and the pattern of deterioration over time is complex, it is very difficult to maintain infrastructure as 'minimum maintenance state' or 'minimum performance state' by the current way of management. 'Asset Management' shall be performed not only by a technical perspective, but also by an accounting perspective such as cost and asset value. However, due to lack of awareness of 'asset management' among stakeholder, only technical perspective management is being carried out in practice. In order to effectively manage infrastructure assets, complex consideration of various asset value factors such as budget and service as well as safety and durability are required. In this paper, we presented a theory to evaluate and quantify the road network value for efficient asset management of the road network. We also presented a method of simulation to apply the theory presented in this paper. Through simulation and the results derived from this study, it is possible to specify the budget for the future national asset management, and to optimize the strategy for the management of old road facilities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.829-837
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• 2011

Construction cost estimation in planning phase which calculates the cost for performing construction tasks is used for various ways. Meanwhile, in the case of road construction, the existing cost estimating method in early phase based on numerical mean value of the past is not accurate to be used. This paper propose neural network model for estimating road construction cost in planning phase to solve the limit of current cost estimating method. The model was designed using past road construction bidding records, and variables of model were optimized through trial and error. The estimation result of the model was compared with regression analysis and government's standard and it was verified that the model is better in accuracy. It is expected that the proposed model will be used for road cost estimation in planning phase.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.84-99
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• 2021

As the micro-mobility market grows, the demand for route guidance, that includes uphill information as well, is increasing. Since the climbing angle depends on the electric motor uesed, it is necessary to establish an uphill road DB according to the threshold standard. Although road alignment information is a very important element in the basic information of the roads, there is no information currently on the longitudinal slope in the road digital map. The High Definition(HD) map which is being built as a preparation for the era of autonomous vehicles has the altitude value, unlike the existing standard node link system. However, the HD map is very insufficient because it has the altitude value only for some sections of the road network. This paper, hence, intends to propose a method to generate the road longitudinal slope using currently available data. We developed a method of computing the longitudinal slope by combining the digital elevation model and the standard link system. After creating an altitude at the road link point divided by 4m based on the Seoul road network, we calculated individual slope per unit distance of the road. After designating a representative slope for each road link, we have extracted the very steep road that cannot be climbed with personal mobility and the slippery roads that cannot be used during heavy snowfall. We additionally described errors in the altitude values due to surrounding terrain and the issues related to the slope calculation method. In the future, we expect that the road longitudinal slope information will be used as basic data that can be used for various convergence analyses.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.34-43
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• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.151-166
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• 2020

Various experience keywords were derived through text mining analysis of two National Ecological and Cultural Exploration Routes. The results of this study were drawn as follows: The interaction between the experience keywords was analyzed by the degree centrality, closeness centrality, and betweenness centrality value calculated through the centrality analysis of the research site experience keywords. First, In the text mining analysis, 'walking' appeared as the top keyword in the I, II, and III periods of the two target areas. The keywords related to the stay type of "rental cottage" and "recreational forest" were derived for Masil Road in relation to accommodation facilities. However, the keywords related to the accommodation were not derived in Gubul Road. Second, as a result of the centrality analysis, the degree centrality of the keywords "walking", "sea", "look", "salt flats" of Masil Road and "walking", "lake" and "park" of Gubul Road was high. The keywords located at the center are "walking" and "sea" in the Masil Road, and "walking" in the Gubul Road. As an influential keyword, Masil Road is "experience" and Gubul Road is "history". Third, According to the results of the analysis, the keywords that appeared at the top of the Gubul Road are derived from the keywords related to the 1 ~ 8 course, and it is judged that the visitors are visiting the 1 ~ 8 course trail evenly. However, the Gubul Road only appears in the top keyword only for a few courses. Through this, it seems that three courses are intensively visited as the main course of 6 Gubul Road, 6-1 Gubul Road, and 8 Gubul Road.

• Journal of the Economic Geographical Society of Korea
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• v.24 no.3
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• pp.243-258
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• 2021

By using the trade in value-added(TiVA) database and employing social network analysis, this paper analyzes changes in global trade to be triggered by the Belt and Road Initiative (BRI) and the US-China trade war. The main results are summarized as follows. First, the BRI will help maintain China's core position as the world's manufacturing hub, and will strengthen Europe's service industry capabilities within the global value chain(GVC) network. Second, the US R&D industry, US wholesale and retail industries, and Germany's automobile industry were considered the most influential industries in the GVC network during the 1995-2011 period, and will retain their status until 2049, when the US-China trade war and the BRI are reflected. Third, the increase of the number of communities shows that the BRI might spur fragmentation of the production process. Finally, community structures of inter-industry trade relations, including China's electronics industry, Germany's automobile industry, and US R&D, show important features that are related to the competiveness of each country's service industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.57-68
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• 2018

A system to prevent disasters by collecting and analyzing environmental information such as road surface sedimentation, sinkholes, collapse risk of bridges, temperature and humidity around tram station is continuously monitored by monitoring the condition of road surface when constructing tram which is one of the urban railways. In this paper, we propose a wireless network security management system for tram roads based on LPWA that can recognize risk factors of road surface, bridge and tram station of tram in advance and prevent risk. The proposed system consists of a sensor node that detects the state of the tram road surface, a gateway that collects sensor information, and a safety management system that monitors the safety and environmental conditions of the tram road surface, and applies the low power long distance communication technology. As a result of comparing the proposed system with the LTE system in the field test, it was confirmed that there is no significant difference between the sensor information value and the critical alarm level in the monitoring system.

• Journal of Soil and Groundwater Environment
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• v.15 no.2
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• pp.18-23
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• 2010

Usability of soil quality monitoring network for ascertaining soil quality changes was evaluated by analysing soil quality monitoring results. Tolerance limits of soil quality monitoring results from 1997 to 2007 were calculated and compared with Korean soil quality standards. This study determined that soil quality was changed if the upper 95% tolerance limit value was greater than the soil quality standard. Fluoride most frequently exceeded the soil quality standard and nickel, zinc, arsenic, copper, lead and cadmium were followed. Analysis on land use showed that tolerance limits of industrial land use most frequently exceeded the soil quality standards and residential, road and various land uses then frequently exceeded. Tolerance limits of land uses expecting high contaminant loads frequently exceeded the soil quality standards. This fact imply that the soil quality monitoring network generates reasonable data to represent change in Korean soil quality. This study also suggested that representative sampling from well identified points should be done to improve data reliability and accurately ascertain soil quality changes.