• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.7
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• pp.791-799
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• 2016

The purpose of this work is to analyse the impact of new accident risks on maritime safety in Korea. The new accident risks have been induced from new/rare or unprecedented events in world maritime transportation, as identified by 46 experts in the previous study. To measure the impact of these new accident risks on maritime safety in Korea, the statistical accident data reported by the Korean Maritime Safety Tribunals (KMST) has been used for calculation, and the concept of Risk Index (RI) = Frequency Index (FI) + Severity Index (SI)established in a Formal Safety Assessment (FSA) by the IMO has also been introduced. After calculating two kinds of weight for FI and SI from the statistical accident data, high ranked scenarios were identified and their relationships between new risks and these scenarios were analysed. The results from this analysis showed, the root cause of the top-ranked scenario to be "developing high technology", which leads to "shorten cargo handling time". These results differed from optimum RCOs such as "business competition" and "crewing problems" which were identified in the previous study.

• Journal of Radiation Protection and Research
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• v.27 no.3
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• pp.189-198
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• 2002

The time for evacuation of residents in emergency planning zone(EPZ) of Uljin nuclear site in case of a radiological emergency was estimated with traffic analysis. Evacuees were classified into 4 groups by considering population density, local jurisdictions, and whether they ate residents or transients. The survey to investigate the behavioral characteristics of the residents was made for 200 households and included a hypothetical scenario explaining the accident situation and questions such as dwelling place, time demand for evacuation preparation transportation means for evacuation, sheltering place, and evacuation direction. The microscopic traffic simulation model, CORSIM, was used to simulate the behavior of evacuating vehicles on networks. The results showed that the evacuation time required for total vehicles to move out from EPZ took longer in the daytime than at night in spite that the delay times at intersections were longer at night than in the daytime. This was analyzed due to the differences of the trip generation time distribution. To validate whether the CORSIM model fan appropriately simulate the congested traffic phenomena assumable in case of emergency, a benchmark study was conducted at an intersection without an actuated traffic signal near Uljin site during the traffic peak-time in the morning. This study indicated that the predicted output by the CORSIM model was in good agreement with the observed data. satisfying the purpose of this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.3
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• pp.9-23
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• 2015

A lane change maneuver which has a high driver cognitive workload and skills sometimes leads to severe traffic accidents. In this study, the Advanced Lane Change Assist System (ALCAS) was developed to assist with the automatic lane changes in merging sections which is mainly based on an automatic control algorithm for detecting an available gap, determining the Optimal Lane Change Start Point (OLCSP) in various traffic conditions, and positioning the merging vehicle at the OLCSP safely by longitudinal automatic controlling. The analysis of lane change behavior and modeling of fundamental lane change feature were performed for determining the default parameters and the boundary conditions of the algorithm. The algorithm was composed of six steps with closed-loop. In order to confirm the algorithm performance, numerical scenario tests were performed in various surrounding vehicles conditions. Moreover, feasibility of the developed system was verified in microscopic traffic simulation(VISSIM 5.3 version). The results showed that merging vehicles using the system had a tendency to find the OLCSP readily and precisely, so improved merging performance was observed when the system was applied. The system is also effective even during increases in vehicle volume of the mainline.

• Journal of Environmental Science International
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• v.19 no.5
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• pp.591-605
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• 2010

This study were to simulate major criteria air pollutants and estimate regional source-receptor relationship using air quality prediction model (TAPM ; The Air Pollution Model) in the Seoul Metropolitan area. Source-receptor relationship was estimated by contribution of each region to other regions and region itself through dividing the Seoul metropolitan area into five regions. According to administrative boundary, region I and region II were Seoul and Incheon in order. Gyeonggi was divided into three regions by directions like southern(region III), northern(IV) and eastern(V) area. Gridded emissions ($1km{\times}1km$) by Clean Air Pollicy Support System (CAPSS) of National Institute of Environmental Research (NIER) was prepared for TAPM simulation. The operational weather prediction system, Regional Data Assimilation and Prediction System (RDAPS) operated by the Korean Meteorology Administration (KMA) was used for the regional weather forecasting with 30km grid resolution. Modeling period was 5 continuous days for each season with non-precipitation. The results showed that region I was the most air-polluted area and it was 3~4 times more polluted region than other regions for $NO_2$, $SO_2$ and PM10. Contributions of $SO_2$ $NO_2$ and PM10 to region I, II and III were more than 50 percent for their own sources. However region IV and V were mostly affected by sources of region I, II and III. When emissions of all regions were assumed to reduce 10 and 20 percent separately, air pollution of each region was reduced linearly and the contributions of reduction scenario were similar to those of base case. As input emissions were reduced according to different ratio - region I 40 percent, region II and III 20 percent, region IV and V 10 percent, air pollutions of region I and III were decreased remarkably. The contributions to region I, II, III were also reduced for their own sources. However, region I, II and III affected more regions IV and V. Shortly, graded reduction of emission could be more effective to control air pollution in emission imbalanced area.

• Economic and Environmental Geology
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• v.54 no.5
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• pp.515-533
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• 2021

Based on the supply location, demand location, and transportation network, aggregate supply-demand characteristics and aggregate distribution status were analyzed from the results of the closest distance, service areas, and location-allocation scenarios using GIS network analysis. As a result, it was found that the average transport distance of aggregates from the supplier was 6 km on average, the average range of 7 km for sand, and 10 km for gravel was found to reach the destination. In particular, the simulated service area covers about 92% in Seoul-Gyeonggi Province, 85% in Busan-Ulsan-Gyeongnam Province, and more than 90% in Daejeon-Sejong-Chungnam Province. These results have a significant implication in quantitatively interpreting primary data on aggregate supply-demand. Furthermore, these results suggest the possibility of a wide-area quantitative analysis of aggregate supply regions necessary for establishing a basic aggregate plan. The results also evaluated by the site-allocation scenario show that aggregate supply may be possible through companies less than 200 with large-amounts quarries, which is the 700 companies currently supplying small amounts of aggregates on the country. Therefore, in terms of distribution of aggregates, a policy approach is needed to form an appropriate market for regions with high and low density of aggregate supply services, and the necessity of regional distribution and re-evaluation is suggested through an aggregate supply analysis demand across the country. Furthermore, in analyzing the supply-demand network for the aggregate market, additional research is needed to establish long-term policies for the aggregate industry and related industries.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.149-156
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• 2021

This study collected and analyzed case studies related with the use of spatial information in North Korea to prioritize construction and required supporting processes in order to propose a short-term and long-term road map for the establishment of spatial information in North Korea. Recent research cases related to the prioritization of spatial information development were analyzed, and priority for the construction of infrastructure was derived based on the interconnectedness and relationship of national infrastructure. Due to the inaccessibility and remoteness of North Korea, all of the five studies determined priorities according to questionnaires and consulting of refugees and knowledgeable figures by expert groups. In summary, priority was given to unarmed and transborder areas, major cities, special zones, and development zones, while in terms of facilities, priority was given to power communication, railroads, water and sewage architectural buildings, roads, and dams. In the case of prioritizing the establishment of national infrastructure for the unified Korean Peninsula, the development of major areas, ports, and the related city-level spots to develop a line that promotes the sharing of routes such as transportation networks, water, and energy, thus leading to a scenario involving the development of cotton at an urban and national level.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.314-323
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• 2022

Vessel Traffic Services (VTS) operators perform traffic management tasks using VTS systems and sensor equipment designated as VTS facilities to promote the safety and efficiency of vessel traffic. The necessary VTS information for effective operations could be obtained through the additional access of various information channels other than the designated VTS facility. To unify these various information access windows, the development of the VTS cloud system is in progress. In this study, the operational information analysis for VTS was performed through VTS tasks-facility linkage analysis to identify the user required information according to the introduction of the VTS cloud system. The VTS task analysis was performed through research of the international and domestic literature, and expert interviews. The necessary information were identified and linked according to the VTS facilities. As a result of the analysis, 37 categories of necessary information were identified for internal and external information windows, and 8 information windows were selected other than the present VTS equipment. The identified user requirements would be applied to the structure design of the VTS cloud system. In the future, it is necessary to update user requirements through scenario-based user operation analysis and to conduct additional research on the system interface design.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.142-156
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• 2024

The transition to electric vehicles is a crucial step toward achieving carbon neutrality in the transportation sector. Adequate charging infrastructure at residential locations is essential. In South Korea, the predominant form of housing is multifamily dwellings, necessitating the provision of public charging stations for numerous residents. Although the government mandates the availability of charging facilities and designated parking areas for electric vehicles, it bases the supply of charging stations solely on the number of parking spaces. Slow chargers, mainly 3.5kW charging outlets and 7kW slow chargers, are commonly used. While the former is advantageous for installation and use, its slower charging speed necessitates the coexistence of both types of chargers. This study presents an optimization model that allocates chargers capable of meeting charging demands based on daily driving distances. Furthermore, using the metaheuristic algorithm Tabu Search, this model satisfies the optimization requirements and minimizes the costs associated with charger supply and usage. To conduct a case study, data from personal travel surveys were used to estimate the driving distances, and a hypothetical charging scenario and environment were set up to determine the optimal supply of 22 units of 3.5kW charging outlets for the charging demands of 100 BEVs.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.