• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.29-37
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• 2010

Ramp metering control is the most representative strategy of uninterrupted flow control and management system. Ramp metering is to adjust vehicles entering an expressway in such a way that expressway mainline maintains flow stability by regulating ramp vehicles. The effect of two metering strategies, single-entry metering and platoon metering, on mainstream under the same metering rate with pre-timed metering are analyzed by micro-simulation. Platoon metering shows lower performance than single-entry metering under the same metering rate in terms of speed, density, and delay, causes earlier breakdown than single-entry metering. It indicates that the selection of metering type has critical importance as the flow of mainline is high.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.1 s.9
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• pp.1-12
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• 2006

In order to estimate a dynamic origin and destination demand between on and off-ramps in the freeways, a traffic flow theory can be used to calculate a link distribution proportion of traffics moving between them. We have developed a dynamic traffic estimation model based on the three-phase traffic theory (Kerner, 2004), which explains the complexity of traffic phenomena based on phase transitions among free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatiotemporal features. The developed model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We have estimated the link, on and off-ramp volumes at every time interval by using traffic data collected from vehicle detection systems in Korea freeway sections. The analyzed results show that the developed model describes traffic flows adequately.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.664-666
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• 1999

영상 추적(visual tracking)은 로봇의 시각기반제어, 교통정보시스템, 무인감시시스템 등 다양한 분야에 적용 가능하기 때문에 지정된 혹은 운동이 감지된 물체를 지속적이고 빠르게 추적하는 데 목적을 둔다. 이 때 어려운 문제 중 하나는 시간이 지나면서 위치이동은 물론 회전에 와해 물체의 모양이 변한다는 것이다. 이에 본 논문에서는 물체의 3차원 회전에 대응 가능한 실시간 영상추적 알고리듬을 제안한다. 이 알고리듬은 SSD(sum-of-squared differences)를 기반으로 하되, 물체의 배경이 바뀔 때나 물체가 영상추적 윈도우보다 작은 경우에도 추적이 가능하고 3차원 회전에 대응 가능하다. 이것은 3차원 회전으로 인하여 추적목표를 잃어버리는 것을 막기 위하여 기준 영역이 회전할 때 제안된 성능지수에 따라 영상추적 영역과 기준 영상을 새롭게 설정해줌으로써 구현된다. 제안된 알고리듬은 PC기반 실시간 시각시스템에서 성공적으로 실험되었다.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.115-126
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• 2009

The lane-change behavior usually consists of discretionary lane-change and mandatory lane-change types. For the first type, drivers change lanes selectively to maintain their own driving condition and the second type is the case that the drivers must change the current lane, which can occur in recurrent congestion sections like merging and weaving sections. The mandatory lane-change behavior have a great effect on the operation condition of freeway. In this paper, we first generate data such as traffic volumes, speeds, densities, and the number of lane-change within the merging and weaving sections using the data of individual vehicle collected from time-lapse aerial photography. And then, the data is divided into the stable and congested flow by analyzing the speed variation pattern of individual vehicles. In addition, the number of lane-changing from ramp to mainline within every 30-meter interval is investigated before and after traffic congestion at study sites and the distribution of lane-changing at each 30-meter point is analyzed to identify the variation of lane-changing ratio depending on the stable and congested flows. To recognize the effect of mainline flow influenced by ramp flow, this study also analyzes the characteristics of the lane-changing distributions within the lanes of mainline. The purpose of this paper is to present the basic theory to be used in developing a lane-changing model at the merging and weaving sections on freeways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.53-65
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• 2004

A series of accidents, which are non-recurrent and non-anticipated, are called incidents. These incidents make standard traffic flows interrupt, which result in the decrease of road capacity and a number of social and economic costs, such as the traffic congestion and air pollution. In order to prevent the hazard of incidents, domestic and foreign traffic management center are likely to opt auto-sense system with algorithms of auto-incident sense. However, it is evaluated that the algorithms have a low function with frequent wrong alarms, even if they accurately ry to speculate the incidents. In the case of bottleneck which has lack of road capacity, compared with other roads, due to inefficient road structured over-capacity of the demand of on-off ramp, the incidents regularly take place. Nonetheless, it can be more difficult to speculate the auto-incidents sense owing to similar incidents, such as the queue of in-out flows of cars and the change of road line. Throughout this research, the function of the model has improved excluding near road line in the module of the incidents which is based on the auto-incidents algorithms during the sense of the congestion of ramp areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.