• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.117-117
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• 2010

외뿔소자리 신성 1975는 발견 당시에는 백색왜성과 적색왜성의 근접쌍성계인 것으로 알려졌으나, 엑스선 관측과 그에 따른 표준 정류 상태 원반 모형에 근거한 해석으로 중성자별 또는 블랙홀의 가능성이 대두되었다. 곧이어, 광학관측에 의하여 블랙홀 이중성임이 간접적으로 동정되었다. 최근 들어 1975젼 당시의 많지 않은 전파 관측 자료들을 재해석하여 상대론적인 물질 분출 현상, 즉 마이크로퀘이사 현상이 있었을 가능성이 관측적으로 제시되었다. 우리는 시간 종속적 열적 불안정성 모형을 바탕으로 수치모형을 계산하여 관측적으로 제시된 물질분출의 가능성을 이론적으로도 제시할 수 있음을 보인다

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.94-104
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• 2010

The capacity of highway is restricted and traffic congestion is caused by increasing traffic demand. Also, greenhouse gases are increased by traffic congestion. CDM (Clean Development Mechanism) is an idea of interest to reduce greenhouse gases. However, CDM's cases applied in traffic field are rare. Thus, it is necessary that methodology to reduce greenhouse gas should be developed and applied to CDM. A methodology for identifying greenhouse gas emissions was developed in this paper. This methodology was developed on the basis of baseline methodology registered at UN. Travel time and speed in the conventional traffic condition and in the automated traffic condition are compared by BPR function. The calculated speed applied to emission factor equation and then $CO_2$ emissions was calculated. A simulation was executed to evaluate the validity of the developed methodology. In the result, advanced vehicle's $CO_2$ emissions are more than conventional vehicle's $CO_2$ emissions in the stable flow condition. However, advanced vehicle's $CO_2$ emissions are less than conventional vehicle's $CO_2$ emissions in the unstable flow condition. It is assure that capacity of highway is enhanced and efficiency of highway is improved by adopting advanced safety vehicle in the smart road.

• Journal of Korean Port Research
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• v.14 no.4
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• pp.419-427
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• 2000

Today travel demand continues to increase with spread of economic zones. Also, urban freeway plays an important role in intra-zone transportations as a major corridor in a big city. However, most of urban freeways experience a severe congestion with the excess of inflowing or outflowing traffic through freeway ramps. The purpose of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the speed predictive models on the ramp junctions of urban freeway under the intelligent transportation system(ITS) settings. From the analyses of traffic characteristics following results were obtained: ⅰ) 24 hours average traffic characteristics flow, occupancy, speed under the ITS settings showed about 40%, 38%, 8.8% increase each on urban freeway junctions period when compared with that under the non-ITS settings each other. Free flow speed and traffic flow on the mainline sections of urban freeway under the ITS settings also showed about 20% and 17% increase when compared with that under the non-ITS, respectively. ⅱ) The upstream when compared speed( $S_{u}$)and downstream occupancy( $O_{d}$) were especially shown to have higher explanatory powers on the stable flow ramp junctions, but the upstream speed( $S_{u}$) and downstream flow( $V_{d}$) were especially shown on the unstable flow ramp junctions of urban freeway under the ITS settings.ngs.ngs.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.145-155
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• 1998

대도시 가로망의 대부분은 신호교차로와 신호교차로가 연결되는 가로(link)로 구성되어 있어 가로의 통행시간은 가로의 주변여건, 차량간의 상호작용 및 교통신호등과 같은 요소에 영향을 받게된다. 따라서 본 연구의 목적은 대도시 가로망에서 신호 연동체계로 운영되는 가로의 통행시간을 예측할 수 있는 모형을 개발하는데 있다. 본 연구는 대구광역시 가로망을 대상으로 연동가로의 교통흐름을 가장 잘 나타내는 Greenberg모형을 이용하여 연동가로의 통행시간 모형을 도출하였다. 도출된 연동가로의 통행시간 모형은 임계통행시간$(t_m$)과 교통량 대가로최대교통량비$(q/q_m)$의 함수로 이루어졌다. $t_m$모형은 안정류상태의 통행시간 및 불안정류상태의 통행시간의 비를 이용하여 개발하였고 가로 용량모형은 상류부와 하류부의 신호조건에 따른 변수와 가로길이를 변수로 하는 모형을 개발하였다. 개발된 모형은 대구광역시를 대상으로 조사한 12개의 연동가로의 자료를 적용하여 연동으로 운영되는 가로의 통행시간 모형을 도출하였다. 도출된 통행시간 모형은 도로용량에 제시한 모형에 비하여 간단하게 가로의 통행시간을 추정할 수 있으며 교통계획에 적용되는 통행시간모형 비하여 가로의 신호 및 운영조건을 포함한 세부적인 통행시간 모형이다.

• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.