• 해양환경안전학회지
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• 제21권3호
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• 한국시뮬레이션학회논문지
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• 제29권3호
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• pp.41-48
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• 2020

본 논문에서는 고속도로 통행량을 보다 정확하게 예측하기 위한 새로운 방법으로 통행량에 대한 누적분포함수를 이용한 동적 예측 알고리즘을 제시한다. 여기서 누적분포함수의 근사함수를 수치적 방법인 내츄럴 큐빅 스플라인(natural cubic spline) 보간법과 레벤버그-마쿼트(Levenberg-Marquardt) 방법을 통해 얻는다. 이 알고리즘은 금융수학에서 활용하는 누적 분포함수를 이용한 난수 생성 알고리즘을 통행량 예측에 알맞도록 새롭게 구조화한 것이다. 이 알고리즘으로 고속도로 통행량을 시뮬레이션하면 실제 통행량과 매우 흡사한 결과를 얻을 수 있음을 확인할 수 있다. 따라서 이 알고리즘은 고속도로뿐만 아니라 통행량 예측이 필요한 다양한 분야에서 활용할 수 있는 새로운 알고리즘이다.

• 대한수학회지
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• 제35권3호
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• pp.727-740
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• 1998

Controlling traffic lights at a bottleneck, in [5] a time of open passage is called optimal, if it minimizes the first moment of the asymptotic distribution of the queue length. The discussion of the first moment as function of the time of open passage is based on an analysis of the behavior of a fixed point when varying control parameters and delivers theoretical and computational aspects of the traffic problem.

• Ocean Systems Engineering
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• 제12권3호
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• pp.267-284
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• 2022

A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.

• 대한교통학회지
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• 제29권3호
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• pp.83-91
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• 2011

이 논문은 확률론적인 방법을 이용하여 동적 통행시간(dynamic travel time) 모형을 도출한다. 동적 통행시간 모형은 차량의 통행시간은 도로 공간상에서의 교통흐름 분포에 따라, 또는 통행구간 출발점에서 시간에 대한 교통흐름의 분포에 따라 결정된다고 가정하여 얻어진다. 이 모형들에서 교통흐름의 분포가 차량의 통행시간에 미치는 정도를 나타내는 확률밀도함수(probability density function)는 여러 가지 형태의 도입될 수 있으나 지수분포를 따른다고 가정한다.

• 도시과학
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• 제11권2호
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• pp.9-17
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• 2022

The purpose of this study is to develop an index for evaluating the function of a new motorway using the travel distance frequency distribution (TLFD) calculated using the vehicle travel route big data, and to overcome the limitations of the evaluation through the existing traffic volume. The mobility evaluation index of motorways was developed by applying it to the TLFD data table in 2019. The smaller the value of the mobility evaluation index of the link is calculated, the more it is a link with mainly short-distance travel, and the higher the value of the mobility evaluation index, the more it means a link with mainly long-distance travel. The accessibility evaluation index was calculated through the result of the mobility evaluation index of all motorways developed, and all motorways were grouped into three groups using K-means clustering. Group A was found to exist inside a large city and consisted of motorways with many short-distance traffic, Group B was investigated as acting as an arterial between groups, and Group C was classified as a motorway consisting mainly of long-distance traffic connecting large cities and large cities. This study is significant in developing a new motorway function evaluation index that can overcome the limitations of motorway function evaluation through the existing traffic volume. It is expected that this study can be a reasonable comprehensive indicator in the operation and planning process of motorways.

• Management Science and Financial Engineering
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• 제15권1호
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• pp.51-69
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• 2009

A network model and a Genetic Algorithm (GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing a non-linear objective function with the linear constraints. In the model, the flow-conservation constraints are utilized to restrict the solution space and to force the link flows become consistent to the traffic counts. The objective of the model is to minimize the discrepancies between two sets of link flows. One is the set of link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links. The other is the set of link flows those are estimated through the trip distribution and traffic assignment using the path flow estimator in the logit-based SUE. In the proposed GA, a chromosome is defined as a real vector representing a set of Origin-Destination Matrix (ODM), link flows and route-choice dispersion coefficient. Each chromosome is evaluated by the corresponding discrepancies. The population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment technique is used during the crossover and mutation.

• Journal of applied mathematics & informatics
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• 제8권1호
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• pp.285-295
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• 2001

We consider a queueing system under overload control to support bursty traffic. The queueing system under overload control is modelled by MMBP/D1/K queue with two thresholds on buffer. Arrival of customer is assumed to be a Markov-modulated Bernoulli process (MMBP) by considering burstiness of traffic. Analysis is done in discrete-time case. Using the generating function method, we obtain the stationary queue length distribution. Finally, the loss probability and the waiting time distribution of a customer are given.

• 해양환경안전학회지
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• 제21권2호
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• pp.179-187
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• 2015

본 연구의 목적은 통항분포함수 계산 시 적용하는 기준선의 방향과 기준점의 수평위치 및 수직위치에 따라서 통항분포함수가 변하는 양상을 식별하기 위한 것이다. 목포항 입구에 있는 항로를 대상으로 2013년 1월달의 AIS 실측자료를 이용하여 기준선의 방향(${\theta}$), 수평위치($\mathfrak{L}_H$) 및 수직위치($\mathfrak{L}_V$) 등의 3가지 변수가 통항분포함수의 평균($\bar{x}$)과 표준편차(${\delta}$)에 미치는 영향을 실험하였다. 실험결과, ${\theta}$에 따라 추출되는 샘플 데이터가 달라지기 때문에 ${\theta}$의 변화에 따라서 $\bar{x}$와 ${\delta}$가 변화됨을 나타냈고, ${\theta}$에 따른 $\bar{x}$와 ${\delta}$의 변화는 사인(sine)함수 합의 관계로 도출되었다. 또한 항로가 복잡한 해역에서 최적의 통항분포함수를 결정하기 위해서는 ${\delta}$의 변화 값이 최소가 되는 ${\theta}$을 기준선의 방향으로 설정하는 것이 타당함을 알았다. 본 연구의 결과는 정규분포가 보다 더 정량화된 수치로 표현되어 해상교통흐름을 파악하고 해상교통안전관리 의사결정을 위한 기초자료로 활용될 것으로 판단된다.

• Journal of the Optical Society of Korea
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• 제18권4호
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.