• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.23-30
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• 2006

The variation in the traffic volume on any given roads is the reflection of its user's economic activities and life patterns. And traffic volume flows in every hour usually take different charateristics depending on the location and the function of the roads. This study produced the Monthly Adjustment Factor, Weekly Adjustment Factor and Design hourly Factor, each of which is the index indicating the traffic volume charaterirstics on the highways leading to the recreation areas in the mountainous and seaside tourist sites. Applying these results, it might be possible to calculate the optimal AADT (Annual Average Daily Traffic) and DHV (Design Hour Volume), also be a help to establish a traffic management policy. Finally, it hopes to promote new version of KHCM (Korea Highway Capacity Manual) which includes traffic volume characteristics on recreation areas.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.510-518
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• 2015

Weather condition has effect on traffic condition, but there is a lack of research between weather and traffic condition. So, in this study analyzes speed variation according to rainfall intensity in national highway provincial road. The results of the analysis, average speed is reduced about 3.2%. But average speed decrease by maximum 8.8% when traffic volume is below 200vph per direction. Because relatively, free flow traffic speed has greatly deceased according to rainfall intensity in provincial road. Also in this study estimates of speed reduction model according to rainfall and performs the statistical verification. Estimated speed reduction model's slops are gradual when rainfall increased, because average speed is reduced by rainfall when free flow.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.426-428
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• 2022

해상 교통량을 정량적으로 평가하고 추출하기 위한 방법으로 선박 AIS 데이터 기반의 밀집도 분석을 활용하고 있다. 밀집도는 단위시간 당 단위면적에 분포하는 선박 통항량을 계산한 것으로, 일반적으로 그리드 셀 내에 존재하는 선박 항적 포인트 개수, 항적도 라인 길이, 선박 척수 등을 계산한 밀집도 분석 방법과 커널 밀도 추정(Kernel Density Estimation) 방법 등이 있다. 하지만, AIS 데이터의 특징상 선박 속력에 따라 수신 주기가 다르기 때문에 항적이 등간격으로 나타나지 않는 문제점이 있으며, 선박의 이동과 시간의 속성으로 인해 각각의 밀집도 분석 방법은 한계점이 존재한다. 따라서 본 연구에서는 실측 AIS 데이터를 이용하여 다양한 방법의 선박 밀집도 분석을 수행하고 이를 비교하였다. 그 결과, 항적도 라인 길이에 의한 밀집도 분석이 가장 정량적인 방법으로 나타났으며 이를 통항 척수로 변환할 수 있는 선박 밀집도 분석을 개선방안으로 제안한다.

• Journal of Navigation and Port Research
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• v.38 no.1
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• pp.65-71
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• 2014

Gyeongbu and Namhae expressways in the country, are the major arterial highways which are connected with the Busan port in the north-south and east-west directions, respectively, and required to study the traffic characteristics about the hourly volume factors(K-factor) by concentrated midium-size and large-size cargo trucks of 20% or higher in expressways. We therefore attempted to predict the K-factor in expressways through the correlation analysis between K-factor and K-factor estimates on the basis of the short-term VDS data collected at the basic segments of the above major expressways. As a result, power model appeared to be appropriate in predicting K-factor by the K-factor estimate based on VDS data for 7 days with a high explanatory power and validity.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.133-135
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• 2013

장래의 해상교통량에 대한 정확한 예측은 항로설계 및 해상교통의 안전성 평가 측면에서 중요한 요소이다. 본 연구는 신뢰성 있는 해상교통량을 추정하기 위해 시계열 모델의 지수평활법과 ARIMA 모형을 이용하여 모형의 식별 및 진단 방안을 제시하였다. 제시된 방법의 효과를 검증하기 위하여 주요항만인 부산항, 광양항, 인천항, 평택항의 해상교통량을 예측하였다. 그 결과로 부산항은 ARIMA 모형, 광양항은 Winters 승법 모형, 인천항은 단순계절 모형, 평택항은 ARIMA 모형이 더 적합한 모형으로 알 수 있었으며, 각 항만별 계절에 따라 월별 교통량의 차이를 보이는 것으로 분석되었다. 본 연구 결과는 향후 항로 및 항만설계 또는 해상교통 안전성 평가에 보다 신뢰성 있는 추정치를 제공할 수 있을 것으로 보인다.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.59-68
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• 2002

AADT(Annual Average Daily Traffic) can be obtained by using short-term counted traffic data rather than using traffic data collected for 365 days. The process is a very important in estimating AADT using short-term traffic count data. Therefore, There have been many studies about estimating AADT. In this Paper, we tried to improve the process of the AADT estimation based on the former AADT estimation researches. Firstly, we found the factor showing differences among groups. To do so, we examined hourly variables(divided to total hours, weekday hours. Saturday hours, Sunday hours, weekday and Sunday hours, and weekday and Saturday hours) every time changing the number of groups. After all, we selected the hourly variables of Sunday and weekday as the factor showing differences among groups. Secondly, we classified 200 locations into 10 groups through cluster analysis using only monthly variables. The nile of deciding the number of groups is maximizing deviation among hourly variables of each group. Thirdly, we classified 200 locations which had been used in the second step into the 10 groups by applying statistical techniques such as Discriminant analysis and Neural network. This step is for testing the rate of distinguish between the right group including each location and a wrong one. In conclusion, the result of this study's method was closer to real AADT value than that of the former method. and this study significantly contributes to improve the method of AADT estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.133-141
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• 2021

The probe vehicle penetration rate is a required parameter in the estimation of entire volume, density, and queue length from probe vehicle data. The previous studies have proposed estimation methods without point detectors, which are based on probability structures for the locations of probe and non-probe vehicles; however, such methods are poorly suited to the case of multi-lane streets. Therefore, this study aimed to estimate the probe vehicle penetration rate at a multi-lane intersection and introduce a probability distribution of the queue length of each lane. Although a gap between estimates and observations was found, the estimates followed the trend of observations; the estimation could be improved by the correction factor hereafter. This study is expected to be used as a basic study for the estimation of entire volume, density, and queue length at multi-lane intersections without point detectors.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.135-145
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• 2008

As a fundamental input to the travel demand forecasting, OD has been always a concern in obtaining the accurate link traffic volume. Numerous methods were applied thus far without a complete success. Some existing OD estimation techniques generally extract regular samples and expand those sample into population. These methods, however, leaves some to be desired in terms of accuracy. To complement such problems, research on estimating OD using additional information such as link traffic volume as well as sample link use rate have been accomplished. In this paper, a new approach for estimating static origin-destination (OD) using probe vehicle has been proposed. More specifically, this paper tried to search an effective sample rate which varies over time and space. In a sample test network study, the traffic volume error rate of each link was set as objective function in solving the problem. As a key result the MAE (mean absolute error) between expanded OD and actual OD was identified as about 5.28%. The developed methodology could be applied with similar cases. Some limitations and future research agenda have also been discussed.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.297-297
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• 1998

고속도로의 교통혼잡을 관리하기 위해서는 근본적으로 혼잡지점 상류부의 진입교통량을 제어해야 한다. 이를 위한 효과적인 램프미터링 운영전략이나 고속도로 교통정보제공방안을 수립하기 위해서는 혼잡영향권(대기행렬길이)에 관한 신뢰성 있는 데이터가 반드시 필요하다. 고속도로의 대기행렬길이를 산정하기 위해 일반적으로 충격파이론과 Queueing이론을 제시하고 있다. 그러나, 기존의 충격파 이론을 포물선형의 교통량-밀도관계식을 근거로 하고 있어 충격파간에 발생하는 부수적인 충격파를 해석하는 과정이 수학적으로 불가능하여 실질적인 목적으로 사용할 수 없음은 이미 잘 알고 있는 사실이다. 최근에 이러한 한계를 극복할 수 있는 새로운 방법으로 교통량 밀도간의 관계식을 삼각형으로 가정하고 교통량 대신에 누적교통량을 사용하는 Simplified Theory of Kinematic Waves In Highway Traffic이 개발(Newell, 1993)되었지만, 이 방법을 적용하기 위해서는 기본적으로 대상 고속도로 구간의 교통량-밀도관계식을 규명해야 하는 어려움이 있다.(사실 실시간으로 밀도데이터를 수집하기란 불가능하다.) Queueing이론에서 제시하는 대기행렬은 모두 대기차량이 병목지점에 수직으로 정렬하여 도로를 점유하지 않는 Point Queue(혹은 Vertical stack Queue)로서 실제로 도로상에 정렬된 대기행렬(Real Physical Queue)과는 전혀 다르다. 이미 입증된 바 있어, Queueing이론을 이용함은 타당성이 없다. 이러한 사실에 근거하여 본 연구는 고속도로 대기행렬길이를 산정할 수 있는 모형개발을 위한 기초연구로서 혼잡상태의 연속류 특성을 분석하는데 목적이 있다. 이를 위해, 본 연구에서는 서울시 도시고속도로에서 수집한 실제 데이터를 이용하여 진입램프지점의 혼잡상태에서 대기행렬의 증가 또는 감소하는 과정을 분석하였다. 주요 분석결과는 다음과 같다. 1. 혼잡초기의 대기행렬은 다른 혼잡시기에 비해 상대적으로 급속한 속도로 증가함. 2. 혼잡초기의 대기행렬의 밀도는 다른 혼잡시기에 비해 비교적 낮음. 3. 위의 두 결과는 서로 관계가 있으며, 혼잡시 운전자의 행태(차두간격)과 혼잡기간중에도 변화함을 의미함. 4. 교통변수 중에서 대기행렬길이를 산정하는데 적합한 교통변수를 교통량과 밀도로 판단됨. 5. Queueing이론에서 제시하는 대리행렬길이 산정방법인 대기차량대수$\times$평균차두간격은 대기행렬내 밀도가 일정하지 않아 부적합함을 재확인함. 6. 혼잡초기를 제외한 혼잡기간 중 대기행렬길이는 밀도데이터 없이도 혼잡 상류부의 도착교통량과 병목지점 본선통과교통량만을 이용하여 추정이 가능함. 7. 이상에 연구한 결과를 토대로, 고속도로 대기행렬길이를 산정할 수 있는 기초적인 도형을 제시함.

• 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.