• 한국도로학회논문집
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• 제20권1호
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• pp.35-45
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• 2018

PURPOSES : This study examines the performance changes of road networks according to the strength of a disaster, and proposes a method for estimating the quantitative resilience according to the road-network performance changes and damage scale. This study also selected high-influence road sections, according to disasters targeting the road network, and aimed to analyze their hazard resilience from the network aspect through a scenario analysis of the damage recovery after a disaster occurred. METHODS : The analysis was conducted targeting Sejong City in South Korea. The disaster situation was set up using the TransCAD and VISSIM traffic-simulation software. First, the study analyzed how road-network damage changed the user's travel pattern and travel time, and how it affected the complete network. Secondly, the functional aspects of the road networks were analyzed using quantitative resilience. Finally, based on the road-network performance change and resilience, priority-management road sections were selected. RESULTS : According to the analysis results, when a road section has relatively low connectivity and low traffic, its effect on the complete network is insignificant. Moreover, certain road sections with relatively high importance can suffer a performance loss from major damage, for e.g., sections where bridges, tunnels, or underground roads are located, roads where no bypasses exist or they exist far from the concerned road, including entrances and exits to suburban areas. Relatively important roads have the potential to significantly degrade the network performance when a disaster occurs. Because of the high risk of delays or isolation, they may lead to secondary damage. Thus, it is necessary to manage the roads to maintain their performance. CONCLUSIONS : As a baseline study to establish measures for traffic prevention, this study considered the performance of a road network, selected high-influence road sections within the road network, and analyzed the quantitative resilience of the road network according to scenarios. The road users' passage-pattern changes were analyzed through simulation analysis using the User Equilibrium model. Based on the analysis results, the resilience in each scenario was examined and compared. Sections where a road's performance loss had a significant influence on the network were targeted. The study results were judged to become basic research data for establishing response plans to restore the original functions and performance of the destroyed and damage road networks, and for selecting maintenance priorities.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1249-1251
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• 2003

In this paper, a framework for creating road network from road edges is proposed. The present framework mainly includes two modules: road modeler and network generator. Road modeler creates the road polygons from the original road edges, and network generator performs converting road polygons to road network with good connectivity at all intersections. A prototype system is also built, and some experimental results are also presented to demonstrate the effectiveness of the proposed framework.

• 한국측량학회지
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• 제36권5호
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• pp.335-341
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• 2018

The KDE (Kernel Density Estimation) technique in GIS (Geographic Information System) has been widely used as a method for determining whether a phenomenon occurring in space forms clusters. Most human-generated events such as traffic accidents and retail stores are distributed according to a road network. Even if events on forward and rear roads have short Euclidean distances, network distances may increase and the correlation between them may be low. Therefore, the NKDE (Network-based KDE) technique has been proposed and applied to the urban space where a road network has been developed. KDE is being studied in the field of business GIS, but there is a limit to the microscopic analysis of economic activity along a road. In this study, the NKDE technique is applied to the analysis of urban phenomena such as the density of shops rather than traffic accidents that occur on roads. The results of the NKDE technique are also compared to pedestrian networks and road centerline networks. The results show that applying NKDE to microscopic trade area analysis can yield relatively accurate results. In addition, it was found that pedestrian network data that can consider the movement of actual pedestrians are necessary for accurate trade area analysis using NKDE.

• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• 한국ITS학회 논문지
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• 제20권1호
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• pp.132-145
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• 2021

본 연구는 효율적인 도로관리를 위해 이용자 중심의 핵심적인 도로망을 분석하고 개선 필요구간을 도출하고자 하였다. 기존의 도로관리는 혼잡을 바탕으로 한 관리자 중심의 다원화된 도로관리체계로 운영되어 왔다. 따라서 본 연구에서는 이용자 중심의 관점에서 여객이동, 물류이동, 관광이동 중심의 핵심도로망을 선정하였으며 서비스 수준(LOS) 분석, EPDO 사고율을 이용한 교통사고 안전성 분석, 네트워크 서비스 분석 등을 통해 개선 필요구간을 도출하였다. 이를 통해 핵심도로망 구축과 이용자 중심의 효율적인 도로 건설·관리체계 구축의 필요성을 강조하고 개선방안을 제시하였다.

• 한국측량학회지
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• 제37권2호
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• pp.91-97
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• 2019

In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2016년도 춘계 학술논문 발표대회
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• pp.241-242
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• 2016

Recently, the interests in safety and prevention from disaster are increasing. In particular, lifeline networks such as water line and sewerage, electricity, gas, and road would be damaged from a disaster. If the lifeline networks do not work in normal, national public service will not properly function. Researches in social network analysis have been conducted for analyzing the interdependency between individuals since 1970s. These network analysis are utilized to investigate a spread of information and disease. However, it is hard to discover the analyzed cases including characteristics of nodes of networks in the area of transportation and disaster. Therefore, this study conducts network analysis of flooded road with flooding scenarios, investigates safe evacuation routes in flooded road network, and suggests efficient approaches for preventing damages from a flooding.

• International Journal of Computer Science & Network Security
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• 제24권9호
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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.

• 제어로봇시스템학회논문지
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• 제5권5호
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.775-779
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• 2002

Currently, high-resolution satellite imagery such as KOMPSAT and IKONOS has been tentatively utilized to various types of urban engineering problems such as transportation planning, site planning, and utility management. This approach aims at software development and followed applications of remotely sensed imagery to transportation geographic analysis. At first, GDPA (Gradient Direction Profile Algorithm) and main modules in it are overviewed, and newly implemented results under MS visual programming environment are presented with main user interface, input imagery processing, and internal processing steps. Using this software, road network are automatically generated. Furthermore, this road network is used to transportation geographic analysis such as gamma index and road pattern estimation. While, this result, being produced to do-facto format of ESRI-shapefile, is used to several types of road layers to urban/transportation planning problems. In this study, road network using KOMPSAT EOC imagery and IKONOS imagery are directly compared to multiple road layers with NGI digital map with geo-coordinates, as ground truth; furthermore, accuracy evaluation is also carried out through method of computation of commission and omission error at some target area. Conclusively, the results processed in this study is thought to be one of useful cases for further researches and local government application regarding transportation geographic analysis using remotely sensed data sets.