• 한국지리정보학회지
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• 제7권1호
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• pp.84-93
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• 2004

최근 여러 전문 분야에서 GIS기반으로 구축된 다양한 공간주제정보의 활용 및 분석에 대한 수요가 증가하고 있다. 본 연구에서는 기본적인 도로 관련 레이어 정보를 이용하여 교통지리학적 분석이 가능한 GIS응용 프로그램을 구현하였다. 본 프로그램을 이용하여 행정 구역단위나 사용자가 임의로 설정한 분석 구역의 도로망으로부터 그래프 형의 망 구조에 대한 특성을 정량적으로 표현하는 우회도(circuity)와 접근도(accessibility)의 산정이 가능하다. 우회도는 분석 구역으로 설정된 구역에 존재하는 노드의 지위를 판단하기 위하여 하나의 바람직한 교통망을 기준으로 하여 실제 도로망을 구성하는 노드들이 어느 정도의 차이를 나타내는 가를 정량적으로 파악하기 위한 방법이며, 접근도는 우회도의 분석에 이용되는 같은 레이어 데이터인 그래프 망 구조에 대하여 망 구조에 포함된 모든 노드를 대상으로 하여 각각의 노드 들간의 접근의 용이성을 나타내고자 하는 개념이다. ArcView 3.2a의 개발언어인 AvenueTM를 이용하여,AVX 형식의 extension으로 구현된 프로그램 실행에 필요한 기본 데이터는 교통 데이터 모델에 기반하는 전문적인 교통 데이터베이스 정보를 필요로 하지 않고 수치지도로부터 쉽게 추출할 수 있는 도로 중심선 레이어와 행정 경계 레이어등을 이용할 수 있도록 하였다. 처리 결과로 얻어진 우회도와 접근도는 교통 분야에서 GIS 적용을 위한 공간 분석 방법으로 활용이 가능할 것으로 생각된다.

• 한국대기환경학회지
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• 제32권6호
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• pp.575-582
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• 2016

Seoul city has high population density as well as high traffic congestion, which are vulnerable to exposure of environmental pollutions caused by car traffic. However, recent studies are only on local regions about road traffic and air pollution or health effect of road traffic on residents. Thus, comprehensive study data are needed in terms of overall Seoul regions. In this study utilized the nitrogen dioxide concentration through the national air pollution monitoring network data, 2012 to 2013. It also divided regions into high and low exposure districts via the Origin destination data developed by the Korea transport institute to quantify and evaluate the effect of transport policies and analyzed a correlation of asthma symptoms with high and low exposure districts through raw data of community health survey from the Korea centers for disease control and prevention. Based on the collected data, the pearson's correlation analysis was conducted between air pollution substance concentration and high exposure district and multiple logistic regression analysis was conducted to determine the effect of traffic environment and factors on asthma symptoms of residents. Accordingly, the following results were derived. First, the high exposure district was higher concentrations of nitrogen dioxide ($NO_2$) as per time compared to those of the low exposure district (p<0.01). Second, analysis on correlation between average daily environmental concentration in the air pollution monitoring network and road traffic showed that nitrogen dioxide had a significant positive correlation (p<0.01) with car traffic and total traffic as well as with truck traffic (p<0.05) statistically. Third, an adjusted odds ratio about asthma doctor's diagnosis in the high and low exposure districts was analyzed through the logistic regression analysis. With regard to an adjusted model 2 (adjusted gender, age, health behavior characteristics, and demographic characteristics) odds ratio of asthma doctor's diagnosis in the high exposure district was 1.624 (95% CI: 1.269~2.077) compared to that of the low exposure district, which was significant statistically (p<0.001).

• 생태와환경
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• 제47권4호
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• pp.282-291
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• 2014

본 연구에서는 두루미류의 개체군 및 서식밀도에 영향을 주는 요인 중 자연적 요인과 함께, 인간에 의한 직 간접적인 교란의 영향에 의한 서식분포모델을 도출하고자 하였다. 대상지역은 강원도 철원군의 민간인통제지역이며, 2009년부터 2014년까지 매년 1월의 두루미와 재두루미의 분포를 대상으로 하였다. 두루미류의 서식밀도는 거주지, 군사시설, 통행량이 빈번한 도로와 가까울수록 서식밀도가 감소하였고, 거리가 멀어질수록 서식밀도는 증가하였다. 통행량이 적은 도로, 잠자리와의 거리 및 비닐하우스의 밀도가 증가할수록 두루미류의 서식밀도는 감소하는 경향을 보였다. 또한, 두루미류의 서식밀도는 민간인통제지역 외부보다 내부의 밀도가 높게 나타났다. 전체 요인을 이용해 단계적 진입을 통한 회귀분석의 결과, 두루미는 $3.4{\times}$AV_FE (이용이 가능한 농경지의 면적 ha)+$1.27{\times}$N_RES (거주지와의 거리 km)-$0.54{\times}$CCZ_0 (민통선 내:0-외:1)+$0.4{\times}$N_HTR (통행량이 많은 도로와의 거리)-1.40이었다. 재두루미는 $9.0{\times}$AV_FE-$5.47{\times}$N_LTR (통행량이 낮은 도로와의 거리 km)+$0.49{\times}$N_Lake (저수지와의 거리 km)+1.02이었다. 두루미와 재두루미 모두 공통적으로 이용 가능한 농경지의 면적이 중요하였고, 두루미에서는 거주지와의 거리 및 통행량이 많은 도로와의 거리가, 재두루미에서는 통행량이 낮은 도로와의 거리가 중요한 요인으로 나타났다. 두 가지 종 모두에서 모델에 의한 추정값과 실제 분포를 이용한 밀도 사이에 유의미한 상관관계가 나타났다.

• 한국ITS학회 논문지
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• 제17권5호
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• pp.55-64
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• 2018

운전자의 안전사고에 직접적인 원인이 되고, 차량 파손을 유발시켜 재산상의 피해를 발생시키고 있는 포트홀을 완전 합성곱 신경망 기반의 자동으로 탐지하는 기법을 본 논문에서는 제안한다. 먼저, 실제 국내 도로를 주행하면서 차량에 설치된 카메라를 통하여 학습 데이터셋을 수집하고, 완전 합성곱 신경망 구조를 활용하여 의미론적 분할 형태로 신경망을 학습하였다. 어두운 환경에서 강건한 성능을 보이기 위하여 학습 데이터셋을 밝기에 따라서 증강하여 총 30,000장의 이미지를 학습하였다. 또한, 제안된 자동 포트홀 탐지 기술의 성능을 검증하기 위하여 총 450장의 평가 DB를 생성하였고, 총 네 명의 전문가가 각각의 이미지를 평가하였다. 평가 결과, 제안된 포트홀 탐지 기술은 높은 민감도 수치를 나타나는 것으로 평가 되었으며, 이는 정탐에서 강건한 성능을 보이는 것으로 해석 가능하다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.347-349
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• 2009

Air quality of indoor space environment is affected by various pollutants like as particles and chemical stuffs. The indoor air pollution affects directly the human respiration organs to cause consequently unpleasant mental status. The $CO_2$ concentration level is one of the harmful components of air pollutants. Major factor to increase the $CO_2$ concentration level is the people's breath amount in indoor. The car exhaust gas diffused from the around road also has strong affect on $CO_2$ concentration. There are some other reasons to affect the $CO_2$ concentration change, such as, real-time change of the population movement, closeness to the indoor air flow inlet window and changes in road car traffic amount. A remote monitoring system to measure environmental indoor air pollution concerning on the $CO_2$ concentration was studied and installed realized set-up model. Zigbee network configuration was applied for this system and the $CO_2$ concentration data were collected through USN network. A software program was developed to assure systematic analysis and to display real-time data on web pages. For the experimental test various condition was set up, like as, window opening, stopping air condition operation and adjusting fan heater work, etc. The analysis result showed the relation of various environmental conditions to $CO_2$ concentration changes. The causes to increase $CO_2$ concentration were experimentally defined as windows closing, the stopping air condition system, fan heater operation. To keep the $CO_2$ concentration under the legally required ppm level in public access indoor space, the developed remote measurement system will be usefully applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• 한국BIM학회 논문집
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• 제8권3호
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Structural Engineering and Mechanics
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• 제88권4호
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• pp.323-334
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• 2023

The submerged floating tunnel (SFT) infrastructure has been regarded as an emerging technology that efficiently and safely connects land and islands. The SFT route problem is an essential part of the SFT planning and design phase, with significant impacts on the surrounding environment. This study aims to develop an optimization model considering transportation and structure factors. The SFT routing problem was optimized based on two objective functions, i.e., minimizing total travel time and cumulative strains, using NSGA-II. The proposed model was applied to the section from Mokpo to Jeju Island using road network and wave observation data. As a result of the proposed model, a Pareto optimum curve was obtained, showing a negative correlation between the total travel time and cumulative strain. Based on the inflection points on the Pareto optimum curve, four optimal SFT routes were selected and compared to identify the pros and cons. The travel time savings of the four selected alternatives were estimated to range from 9.9% to 10.5% compared to the non-implemented scenario. In terms of demand, there was a substantial shift in the number of travel and freight trips from airways to railways and roadways. Cumulative strain, calculated based on SFT distance, support structure, and wave energy, was found to be low when the route passed through small islands. The proposed model helps decision-making in the planning and design phases of SFT projects, ultimately contributing to the progress of a safe, efficient, and sustainable SFT infrastructure.

• 대한교통학회지
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• 제26권6호
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• pp.123-132
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• 2008

동적(dynamic) 기종점(origin-destination, OD) 통행량은 다양한 교통분야에 활용이 가능한데, 대표적으로 동적 통행배정모형의 입력자료와 같은 교통계획분야와 실시간 도로교통 운영분야, 그리고 교통수요 관리분야 등에도 사용할 수 있다. 이런 교통정책들을 평가하기 위해서는 정확한 동적 OD통행량의 추정은 무엇보다 중요하며, 이를 위하여 다양한 기법들이 제시되고 있다. 본 연구에서는 최근 새롭게 연구되고 있는 개인이 소지한 통행 단말기 정보를 이용하여 동적 OD통행량을 추정하고 이를 평가하고자 한다. 이를 위하여 동적 OD추정모형을 개발하고 개발된 추정모형과 동적 통행배정모형(DYNASMART-P)을 이용하여 동적 OD통행량을 추정하는데, 동적OD통행량 추정시 이용되는 단말기 정보가 표본자료(sample data)이기 때문에 이를 전수화하는 과정이 포함된다. 본 연구에서 제안한 방법으로 제주시를 대상으로 동적OD통행량을 추정한 결과, 그 가능성을 확인할 수 있었다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.