• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.135-142
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• 2006

The flow-density relations represent equilibrium relations between flow and density in the stationary state. Using individual vehicle data this paper proposed a method to 131ter traffic data in the stationary state and showed flow-density relations produced by the traffic data in the stationary state. The Proposed method is based on the idea that free flow and congested flow show totally different traffic behaviors and time series of the traffic data observed at detection stations. The traffic data collected from the stationary state in the free flow using this filtering method consist in the left branch of the flow-density relation and the traffic data collected from the stationary state in the congested flow consist in the right branch of the flow-density relation. The traffic data in the stationary state skew reproducible flow-density relation in the almost whole range of the traffic flow.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.275-277
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• 2018

선박 및 해상교통관제에 있어서 교통 혼잡구역에 대한 선박교통밀도 예측은 선박충돌사고 예방에 중요하다. 선박 교통밀도 예측정보는 사전에 진입하는 선박들에게 속력조정, 우회항로 이용 등 사전 조치가 가능하다. 본 연구에서는 해상 선박교통상황을 딥러닝 네트워크에 학습한 주의구역 선박교통류 예측 모델(Ship Traffic Extraction Network, STENet)을 제안하여 주의구역의 선박교통류 예측을 수행하고자 한다. STENet 모델 학습을 위해 여수해역 AIS 데이터를 전처리하고, 생성된 입력(해상교통상황)-출력(주의구역 교통밀도) 쌍 데이터를 적용하여 STENet 모델을 학습하였다. 학습된 모델을 이용하여 선박교통류 예측을 한 결과, 중기예측은 표준 절대 오차(mean absolute error)가 0.4-0.5척이 였으며, 장기예측은 0.7-0.8척의 오차로 기존의 Dead Reckoning에 의한 방법보다 50% 이상 교통밀도 예측성능이 향상 되었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.3-10
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• 1998

본 연구에서는 동적통행시간예측을 위한 하나의 방법으로 블록밀도법을 도입하여 가로상의 일정한 구간을 세분화하고 몇 개의 블록으로 분할한 후 교통류를 유체근사화시키고 각 블록의 밀도를 일정시간 마다 갱신해 나가는 방법을 채택하였다. 즉, 각 블록에서 주어진 밀도의 초기치와 최하류부의 블록에서 유출교통량을 이용하여 일정시간 간격으로 모든 블록에서의 밀도를 수정해 가는 유체의 연속방정식의 개념을 도입하였다. 또한, 본 연구에서는 첨단교통시스템에 적용될 동적통행시간 예측을 위해 기존의 연속교통류만을 대상으로 하던 것에서 벗어나 신호등을 포함한 단속교통류를 대상으로 하였다. 또한 교통류의 저밀도 구간과 고밀도 구간을 분리하여 Ele복합모형을 적용하여 교통류를 해석하고 가급적 실제 상황과 유사하게 근접시키고자 하였다. 이 이론을 근거로 구축된 모델에 실제 현장에서 얻어진 교통량, 밀도, 속도 등의 자료들을 투입하고 통행시간의 예측을 도모하였다.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.39-56
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• 2011

This study proposed a traffic accident prediction model developed based on administrative districts of Seoul. The model was to find the relationship between accident rates and the representative land usage of the districts (development density) - the higher the development density (building floor area) is, the higher the traffic accident rate is. The findings showed that traffic accident statistics differ from (1) residential building floor area, (2) commercial building floor area and (3) business building floor area.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.443-450
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• 2009

Uninterrupted facility - since there is a close relationship between traffic volume, speed and density -, when a ramp traffic flow merges into the main line, will change the traffic speed or density, and the corresponding correlational model equation will be changed. Thus, this study, using time and space-series traffic data on areas under the influence of such a merging, identified sections which changed the correlation between speed and density variables, and examined such changes. As a result, the upstream and merging sections showed the "Underwood"-shaped exponent, and the downstream after passing the merging section showed a straight line "Greenshields" model. The downstream section which changed the correlation between speed and density showed a gradual downstream movement phenomenon within 100 m-500 m from the end of the third lane linking with the ramp, as the traffic approached the inner lanes. Also, the upstream section, merging section, and downstream section involving a change showed heterogeneous traffic flows which, in the speed-density model, have a statistically different free flow speed (constant) and a different ratio of free flow speed to jam density (gradient).

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.179-201
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• 2004

Online traffic flow modeling is attracting more attention due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper the flow-density relation is analyzed dynamically and interpreted as a states diagram. The dynamic flow-density relation is quantified by applying fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.49-58
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• 2006

The purpose of this study is to develop transportation-sensitive land use densities in a metropolitan context. It analyses traffic impacts according to 20 different development density scenarios in the downtown Seoul, and estimates the density ceiling. The results identify that the transportation-wise sustainable density in the downtown can be extended up to the FAR level of 460% with an option of 2,000 won congestion charge levied on the downtown area. It also finds that the region-wide sustainability measured by congestion level can be improving as the level of congestion charge increases. Without the congestion charge, however, the density ceiling slides down to 430%. It is recommended that, in order to bring in higher density developments in the region, transportation demand management (TDM) measures are indispensible.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.137-147
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• 2005

This study is to define new road capacity concept, and to develop and propose an estimation method, through the analysis of individual vehicular behaviors in continuum flow. Developments in detection technology enable various and precise traffic data collection. The U.S. HCM (Highway Capacity Manual) method does not require such various and precise traffic data, and outputs only limited results. Alternative capacity concepts, which can be classified into a stochastic model and behavioral or deterministic model, are attempts for modeling some prominent traffic flow features, namely so-called a capacity drop and a traffic hysteresis, using such various and precise traffic data. Yet, no capacity concept up-to-date can describe both features. The analysis of individual vehicular behaviors, including speed-density plot per time lap, traffic flow-speed-density diagram per each sampling interval, time headway distribution, and free flow speed distribution, is performed for overcoming the limits of the previous capacity concepts. A stochastic methods are applied to determine time headway for estimating freeway capacity for traffic control.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.144-152
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• 2018

This study focuses on collecting traffic data using drones to compensate for limitation of the data collected by the existing traffic data collection devices. Feasibility analysis was performed to verify the traffic data extracted from drone videos and optimal methodology for extracting data was established through analysis of various data reduction scenarios. It was found from this study that drones are very economical traffic data collection devices and have strength of determining the level-of-service(LOS) for uninterrupted flow condition in a very simple and intuitive way.