• Korean Journal of Environment and Ecology
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• v.29 no.4
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• pp.615-625
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• 2015

This study examined the effects of wildlife or wildlife species on national park visitors' perception of place attributes and recreational experiences. Data were collected from 502 users of Seongsamjae Rest area and Nogodan Shelter area in Jirisan National Park and 173 college students during May-June 2014 using survey questionnaire. Some simulated photographs of water deer and Asiatic black bears were used for the college student survey. Overall, awareness about wildlife inhabiting in Jirisan National Park (AW) was not related with one's perception of place attributes (PPA) (i.e., crowdedness, naturalness, safety) and types of visitors' recreational experiences. Respondents with higher awareness about the presence of Asiatic black bear (AABB), however, tended to perceive Jirisan National Park as a place that provides 'wild' or 'natural' recreational opportunities compared to those with lower AABB. Differences in PPA (i.e., crowdedness, naturalness, safety) and types of recreational experiences were also found to be influenced by wildlife species. Respondents exposed to bear or water deer tended to perceive their recreational experiences as more 'wild'. Existence of wildlife in Jirisan National Park had a positive effect on the quality of visitors' recreational experiences. Different wildlife species showed different levels of effectiveness to quality enhancement of recreational experience. Some practical implications of the study were discussed from a managerial point of view.

• Korean Journal of Environment and Ecology
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• v.7 no.2
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• pp.181-186
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• 1994

The Shindae resting place-Jibong valley forest in Tokyusan area was studied to investigate forest structure in relation to altitude and part of slope. Forty eight quadrats were set up in the valley forest along altitude of 750m to 1,247m and part of the slope. Density of trees in tree strata decreased as increasing elevation, and mean DBH of trees in tree strata increased as increasing elevation. With increasing elevation the importance values of Quercus mongolica, Acer mono, Fraxinus mandshurica increased, while those of Quercus serrate, Betula schmidtii decreased. As going from lower part to upper part of the slope, the importance values of Quercus mongolica and Symplocos chinensis for. pilosa increased while those of Cornus controversa and Fruxinus mandshurica decreased. The number of species, species diversity and evenness tended to decreased as increasing elevation. The range of similarity indices between elevation belts, and parts of the slope were 55.3~67.1％ and 36.8~71.7%, respectively. According to importance value and cluster analysis, the studied valley forest was classified into four forest communities of Quercu serrata community of lower part of slope of low elevation belt, Quercus mongolica-Quercus serrata community of middle and upper part of slope of low elevation belt, Quercus mongolica-deciduous tree species community of middle and high elevation belt and Quercus mongolica-Rhododendron schzippinbachii community of the top area.

• Journal of Korean Society of Transportation
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• v.34 no.4
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• pp.341-353
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• 2016

Highway is mainly in charge of middle-long distance of vehicular travel. Trip length has shown a growing trend due to increased commute distances by the relocation of public agencies. For this reason, the proportion of driver-driven accidents, caused by their fatigue or sleepiness, are very high on highways. However, existing studies related to accident prediction have mainly considered external factors, such as road conditions, environmental factors and vehicle factors, without driving behavior. In this study, we suggested an accident index (FDR, Fatigued Driving Rate) based on traffic behavior using large-scale Car Navigation path data, and exlpored the relationship between FDR and traffic accidents. As a result, FDR and traffic accidents showed a high correlation. This confirmed the need for a paradigm shift (from facilities to travel behavior) in traffic accident prediction studies. FDR proposed in this study will be utilized in a variety of fields. For example, in providing information to prevent traffic accidents (sleepiness, reckless driving, etc) in advance, utilization of core technologies in highway safety diagnostics, selection of priority location of rest areas and shelter, and selection of attraction methods (rumble strips, grooving) for attention for fatigued sections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.16-30
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• 2019

In this study, we calculated the driving duration of individual vehicles according to the availability of rest facility on highway, and suggested indicators indicating the potential risk of accidents caused by long-term driving with weights based on the number of vehicles by driving duration of individual links. Based on this, the methodology for estimating the appropriate location of the highway rest facility considering the driving duration of individual vehicles was presented. Using the DSRC individual vehicle data collected from the highways, the appropriate location of the rest facility was calculated by considering the driving duration by classifying weekdays and weekends for the Gyeongbu Expressway. The results showed that the weekly and weekend high risk indicators were different. In the case of weekdays, the risk indicators of Gimchun JC to Kumho JC for Busan were high, while for weekends, the risk indicators of Ansung JC to Dongtan JC for Seoul and Ansung IC to Bukchunan IC for Busan were high. This study has great significance in that it provides a framework for detailed analysis of link units by using non-aggregated data of individual vehicle units. In addition, it is significant that the reasonable driving duration reflecting the behavior of individual vehicles was calculated by analyzing the use of rest facilities.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of Wetlands Research
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• v.7 no.1
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• pp.129-138
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• 2005

The flora and vegetation of Jang-cheok wetland (Gyeong-nam) was investigated from April 1. 2004 to Feburary 28. 2005. The vegetation plants collected from this area were 18 taxa : 18 species 17 genera 14 families. Hygrophtes were 2 species 2 genera 2 families. Emergent plants were 6 species 5 genera 6 families. Submersed plants were 1 species 1 genera 1 families. Floating-leaved plants were 5 species 5 genera 5 families. Free-floating plants were 2 species 3 genera 2 families. Dominance of life form was investigated Trapa japonica, Phragmites commnis, Leersia japonica by 5 dominance values and Hydrocharis dubia, Ceratophyllum dmersum were 1 dominance values. The community was investigated 5 communities : Leersia japonica community. Trapa japonica community. Nelumbo nucifera community. Miscanthus sacchariflorus community. Phragmites communis community. Dominance species(Leersia japonica) of Leersia japonica community was investigated $1,89g/m^{2}$(Dry weight) and $1,730ind./m^{2}$(Density). Doninance species(Trapa japonica) of Trapa japonica community was investigated $36,25g/m^{2}$(Dry weight) and $15.20ind./m^{2}$(Density). Dominance species(Nelumbo nucifera) of Nelumbo nucifera community was investigated $30.59g/m^{2}$(Dry weight) and $11.20ind./m^{2}$(Density). Dominance species(Miscanthus sacchariflorus) of Miscanthus sacchariflorus community was investigated $180.50g/m^{2}$(Dry weight) and $124.80ind./m^{2}$(Density). Dominance species(Phragmites communis) of Phragmites communis community was investigated $159.50g/m^{2}$(Dry weight) and $60.00ind./m^{2}$(Density). The predominant species of this area was investigated Trapa. Japonica and the other communities was only small area in the waterside area.