• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.554-563
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• 2015

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.26-49
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• 2018

The purpose of this study was to verify the reliability of the solar radiation model and discuss its applicability to the urban area of Seoul for summer heat stress mitigation. We extended the study area closer to the city scale and enhanced the spatial resolution sufficiently to determine pedestrian-level urban radiance. The domain was a $4km^2$ residential area with high-rise building sites. Radiance modelling (SOLWEIG) was performed with LiDAR (Light Detection and Ranging)-based detailed geomorphological land cover shape. The radiance model was evaluated using surface energy balance (SEB) observations. The model showed the highest accuracy on a clear day in summer. When the mean radiation temperature (MRT) was simulated, the highest value was for a low-rise building area and road surface with a low shadow effect. On the other hand, for high-rise buildings and vegetated areas, the effect of shadows was large and showed a relatively low value of mean radiation temperature. The method proposed in this study exhibits high reliability for the management of heat stress in urban areas at pedestrian height. It is applicable for many urban micro-climate management functions related to natural and artificial urban settings; for example, when a new urban infrastructure is planned.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.786-794
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• 2018

In recent years, in response to rapid technological change, technology planning activities are important for companies to cope with technological competition. However, Small and Medium Enterprises(SMEs) lack the resources and competence to establish their own technology roadmaps. Therefore, government-led roadmaps for SMEs have been proposed to support the R&D direction and R&D capabilities of SMEs. However, SMEs R&D policies are still not well linked with technology roadmaps and R&D programs. In other words, the size of R&D for SMEs is determined according to demand of SMEs regardless of strategic technology roadmap. In this study, we propose a investment prioritization model based on a technology road map to unify R&D investment policy. Using the decision model designed to prioritize strategic technology investments in the SME strategy roadmap, we conduct empirical analysis of strategic technologies in the advanced convergence and green manufacturing. AHP analysis was conducted after questionnaires on the importance of strategic technology and the importance of influential factors to 46 experts such as expert of research institutes and support organizations.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.277-286
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• 2021

Background: As trade increases, the influx of various alien species and their spread to new regions are prevalent and no longer a special problem. Anthropogenic activities and climate changes have made the distribution of alien species out of their native range common. As a result, alien species can be easily found anywhere, and they have nothing but only a few differences in intensity. The prevalent distribution of alien species adversely affects the ecosystem, and a strategic management plan must be established to control them effectively. To this end, hot spots and cold spots were analyzed according to the degree of distribution of invasive alien plants, and major environmental factors related to hot spots were found. We analyzed the 10,287 distribution points of 126 species of alien plants collected through the national survey of alien species by the hierarchical model of species communities (HMSC) framework. Results: The explanatory and fourfold cross-validation predictive power of the model were 0.91 and 0.75 as AUC values, respectively. The hot spots of invasive plants were found in the Seoul metropolitan area, Daegu metropolitan city, Chungcheongbuk-do Province, southwest shore, and Jeju island. Generally, the hot spots were found where the higher maximum temperature of summer, precipitation of winter, and road density are observed, but temperature seasonality, annual temperature range, precipitation of the summer, and distance to river and sea were negatively related to the hot spots. According to the model, the functional traits accounted for 55% of the variance explained by the environmental factors. The species with higher specific leaf areas were more found where temperature seasonality was low. Taller species preferred the bigger annual temperature range. The heavier seed mass was only preferred when the max temperature of summer exceeded 29 ℃. Conclusions: In this study, hot spots were places where 2.1 times more alien plants were distributed on average than non-hot spots (33.5 vs 15.7 species). The hot spots of invasive plants were expected to appear in less stressful climate conditions, such as low fluctuation of temperature and precipitation. Also, the disturbance by anthropogenic factors or water flow had positive influences on the hot spots. These results were consistent with the previous reports about the ruderal or competitive strategies of invasive plants instead of the stress-tolerant strategy. The functional traits are closely related to the ecological strategies of plants by shaping the response of species to various environmental filters, and our result confirmed this. Therefore, in order to effectively control alien plants, it is judged that the occurrence of disturbed sites in which alien plants can grow in large quantities is minimized, and the river management of waterfronts is required.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.209-227
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• 2023

The government continues to announce measures to revitalize smart construction technology based on BIM for productivity innovation in the construction industry. In the design phase, the goal is design automation and optimization by converging BIM Data and other advanced technologies. Accordingly, in the basic design of the Namhae Seomyeon-Yeosu Sindeok National Road Construction Project, a domestic undersea tunnel project, BIM-based design was carried out by developing tunnel design automation technology using 3D spatial information according to the tunnel design process. In order to derive the optimal alignment, more than 10,000 alignment cases were generated in 36hr using the generative design technique and a quantitative evaluation of the objective functions defined by the designer was performed. AI-based ground classification and 3D Geo Model were established to evaluate the economic feasibility and stability of the optimal alignment. AI-based ground classification has improved its precision by performing about 30 types of ground classification per borehole, and in the case of the 3D Geo Model, its utilization can be expected in that it can accumulate ground data added during construction. In the case of 3D blasting design, the optimal charge weight was derived in 5 minutes by reviewing all security objects on the project range on Dynamo, and the design result was visualized in 3D space for intuitive and convenient construction management so that it could be used directly during construction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.75-89
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• 2006

This paper presents a practical and realistic Life-Cycle Cost (LCC) optimum design methodology of steel bridges considering time effect of bridge reliability under environmental stressors such as corrosion and heavy truck traffics. The LCC functions considered in the LCC optimization consist of initial cost, expected life-cycle maintenance cost and expected life-cycle rehabilitation costs including repair/replacement costs, loss of contents or fatality and injury losses, road user costs, and indirect socio-economic losses. For the assessment of the life-cycle rehabilitation costs, the annual probability of failure which depends upon the prior and updated load and resistance histories should be accounted for. For the purpose, Nowak live load model and a modified corrosion propagation model considering corrosion initiation, corrosion rate, and repainting effect are adopted in this study. The proposed methodology is applied to the LCC optimum design problem of an actual steel box girder bridge with 3 continuous spans (40 m+50 m+40 m=130 m), and various sensitivity analyses of types of steel, local corrosion environments, average daily traffic volume, and discount rates are performed to investigate the effects of various design parameters and conditions on the LCC-effectiveness. From the numerical investigation, it has been observed that local corrosion environments and the number of truck traffics significantly influence the LCC-effective optimum design of steel bridges, and thus realized that these conditions should be considered as crucial parameters for the optimum LCC-effective design.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.149-158
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• 2002

For the purpose of preciously describing real time traffic pattern in urban road network, dynamic network loading(DNL) models able to simulate traffic behavior are required. A number of different methods are available, including macroscopic, microscopic dynamic network models, as well as analytical model. Equivalency minimization problem and Variation inequality problem are the analytical models, which include explicit mathematical travel cost function for describing traffic behaviors on the network. While microscopic simulation models move vehicles according to behavioral car-following and cell-transmission. However, DNL models embedding such travel time function have some limitations ; analytical model has lacking of describing traffic characteristics such as relations between flow and speed, between speed and density Microscopic simulation models are the most detailed and realistic, but they are difficult to calibrate and may not be the most practical tools for large-scale networks. To cope with such problems, this paper develops a new DNL model appropriate for dynamic traffic assignment(DTA), The model is combined with vertical queue model representing vehicles as vertical queues at the end of links. In order to compare and to assess the model, we use a contrived example network. From the numerical results, we found that the DNL model presented in the paper were able to describe traffic characteristics with reasonable amount of computing time. The model also showed good relationship between travel time and traffic flow and expressed the feature of backward turn at near capacity.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.93-100
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• 2010

Trip generation is the first step in the conventional four-step model and has great effects on overall demand forecasting, so accuracy really matters at this stage. A linear regression model is widely used as a current trip generation model for such plans as urban transportation and SOC facilities, assuming that the relationship between each socio-economic index and trip generation stays linear. But when rapid urban development or an urban planning structure has changed, socio-economic index data for trip estimation may be lacking to bring many errors in estimated trip. Hence, instead of assuming that a socio-economic index widely used for a general purpose, this study aims to develop a new trip generation model by type based on the market separation for the variables to reflect the characteristics of various zones. The study considered the various characteristics (land use, socio-economic) of zones to enhance the forecasting accuracy of a trip generation model, the first-step in forecasting transportation demands. For a market separation methodology to improve forecasting accuracy, data mining (CART) on the basis of trip generation was used along with a regression analysis. Findings of the study indicated as follows : First, the analysis of zone characteristics using the CART analysis showed that trip production was under the influence of socio-economic factors (men-women relative proportion, age group (22 to 29)), while trip attraction was affected by land use factors (the relative proportion of business facilities) and the socio-economic factor (the relative proportion of third industry workers). Second, model development by type showed as a result that trip generation coefficients revealed 0.977 to 0.987 (trip/person) for "production" 0.692 to 3.256 (trip/person) for "attraction", which brought the necessity for type classifications. Third, a measured verification was conducted, where "production" and "attraction" showed a higher suitability than the existing model. The trip generation model by type developed in this study, therefore, turned out to be superior to the existing one.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.195-202
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• 2011

The purpose of this study is to estimate the capacity of two-lane freeway work zone blocking one lane using traffic flow models of each vehicle-type. Firstly, three traffic flow models of three different vehicle-types were developed using the data collected from each at the beginning and the ending point of the work zone. For each model, the maximum flow rate of three vehicle-types were calculated respectively. Maximum flow rate at the work zone was recalculated using passenger car equivalent value and percentage of vehicle-type. Secondly, traffic flow model using passenger car equivalent volume data was developed using the data collected from each at the beginning and the ending point of the work zone. Maximum flow rate for the work zone was calculated along. Two values of maximum flow rates through the work zone were compared and evaluated as the capacity of the work zone. This study found that the maximum flow rate of the work zone at the beginning point was less than that at the ending point because of impedance such as lane changing behaviors before entering the work zone. The capacity of two-lane freeway work zone blocking one lane was estimated 1,800pcphpl.