• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• Journal of Korean Society of Transportation
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• v.17 no.4
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• pp.59-69
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• 1999

A Freeway corridor is a network consisting of a few Primary longitudinal roadways (freeway or major arterial) carrying a major traffic movement with interconnecting roads which offer the motorist alternative paths to his/her destination. Control measures introduced to ameliorate traffic performance in freeway corridors typically include ramp metering at the freeway entrances, and signal control at each intersections. During a severe freeway incident, on-ramp metering usually is not adequate to relieve congestion effectively. Diverting some traffic to the Parallel surface street to make full use of available corridor capacity will be necessary. This is the purpose of the traffic management system. So, an integrated traffic control scheme should include three elements. (a)on-ramp metering, (b)off-ramp diversion and (c)signal timing at surface street intersections. The purpose of this study is to develop an integrated optimal control model in a freeway corridor. By approximating the flow-density relation with a two-segment linear function. the nonlinear optimal control problem can be simplified into a set of Piecewise linear programming models. The formulated optimal-control Problem can be solved in real time using common linear program. In this study, program MPL(ver 4.0) is used to solve the formulated optimal-control problem. Simulation results with TSIS(ver 4.01) for a sample network have demonstrated the merits of the Proposed model and a1gorithm.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1369-1382
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• 2005

Vehicle segmentation, which extracts vehicle areas from road scenes, is one of the fundamental opera tions in lots of application areas including Intelligent Transportation Systems, and so on. We present a vehicle segmentation approach for still images captured from outdoor CCD cameras mounted on the supporting poles. We first divided the input image into a set of two-dimensional grids and then calculate the feature values of the edges for each grid. Through analyzing the feature values statistically, we can find the optimal rectangular grid area of the vehicle. Our preprocessing process calculates the statistics values for the feature values from background images captured under various circumstances. For a car image, we compare its feature values to the statistics values of the background images to finally decide whether the grid belongs to the vehicle area or not. We use dynamic programming technique to find the optimal rectangular gird area from these candidate grids. Based on the statistics analysis and global search techniques, our method is more systematic compared to the previous methods which usually rely on a kind of heuristics. Additionally, the statistics analysis achieves high reliability against noises and errors due to brightness changes, camera tremors, etc. Our prototype implementation performs the vehicle segmentation in average 0.150 second for each of $1280\times960$ car images. It shows $97.03\%$ of strictly successful cases from 270 images with various kinds of noises.