• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.49-58
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• 2004

The first and perhaps the most critical and perhaps the most important step in the process of predicting future traffic volume in a region (Zone) is to estimate the number of trips generated in from each traffic analysis zone. Most trip generation models for urban transportation planning, and highway in Korea are regression models. In Korea the category analysis has not been tried for last decades since the proper data such as the household travel behavior data have not been collected. Recently, the comprehensive household travel behavior survey such as ${\ulcorner}$1996 The Household Travel Behavior Survey${\lrcorner}$, ${\ulcorner}$2002 The Household Travel Behavior Survey${\lrcorner}$ has been done. In this paper, the cross-classification tables of Seoul Metropolitan Area including the City of Seoul and Kyonggi Province are estimated by the category analysis. The tables are compared with regression models and ${\ulcorner}$2002 The Household Travel Behavior Survey${\lrcorner}$ data in terms of predictive capabilities in Seoul Metropolitan Area. Improvement strategies for trip generation forecast in Seoul Metropolitan Area are proposed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.155-162
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• 2008

This study shows how to estimate the design hour factor when the counting stations don't have all of the hourly volumes such as in a coverage survey. A coverage survey records traffic volume from 1 to 5 times in a year so it lacks the detailed information to calculate the design hour factor. This study used the traffic volumes of permanent surveys to estimate the design hour factor in coverage surveys using correlation and regression analysis. A total 7 independent variables are used : the coefficient of variance of hourly volume, standard deviation of hourly volume, peak hour volume, AADT, heavy traffic volume proprotion, day time traffic volume proportion and D factor. All of variables are plotted on a curve, so it must use non-linear regression to analyze the data. As a result the coefficient of determination and MAE are good at logarith model using AADT.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.52-62
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• 2013

Design Hourly Factor(DHF) is defined as the ratio of design hourly volume(DHV) to Average Annual Daily Traffic(AADT). Generally DHV used the 30th rank hourly volume. But this case DHV is affected by holiday volumes so the road is at risk for overdesigning. Computing K factor is available for counting 8,760 hour traffic volume, but it is impossible except permanent traffic counts. This study applied three method to make DHF, using 30th rank hourly volume to make DHF(method 1), using peak hour volume to make DHF(method 2). Another way to make DHF, rank hourly volumes ordered descending connect a curve smoothly to find the point which changes drastic(method 3). That point is design hour, thus design hourly factor is able to be computed. In addition road classified 3 type for national highway using factor analysis and cluster analysis, so we can analyze the characteristic of DHF by road type. DHF which was used method 1 is the largest at any other method. There is no difference in DHF by road type at method 2. This result shows for this reason because peak hour is hard to describe the characteristic of hourly volume change. DHF which was used method 3 is similar to HCM except recreation road but 118th rank hourly volume is appropriate.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.17-23
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• 2004

교통정책을 평가하기 위해 기본적으로 요구되는 Data 중 가장 근본이 되는 것이 OD이다. 기존의 교통정책을 평가함에 있어서 일반적으로 사용되고 있는 OD는 AADT(Annual Average Daily Traffic) OD이다. 계절별 평일/주말 교통량의 분산이 매우 크다는 것은 기존 조사나 연구로 익히 알려진 사실이며, 또한 사회 경제적인 여건의 변화 및 주 5일제 근무제의 시행 등으로 여가통행의 비중이 높아짐에 따라 평일과 주말의 교통량의 분산은 더욱 커질 것으로 예상된다. 따라서 교통정책을 평가하는 방법도 AADT OD의 일률적인 적용이 아닌 교통량의 계절별 평일/주말의 분산을 적용시킨 OD를 가지고 교통정책을 평가하는 방법이 교통정책을 결정함에 있어 오류를 범할 가능성을 적게 될 것으로 예상된다. 기존 연구에서는 이러한 교통량의 분산의 보정을 지점교통량에 한정하여 보정하고 있어 실질적인 네트워크 분석에 적용하기에는 무리가 있다. 이에 본 연구에서는 관측된 TCS Data를 이용하여 계절별 평일/주말의 OD 교통 패턴을 분석하여 계절별 평일/주말의 OD 교통패턴을 반영할 수 있는 보정계수를 산출하고 산출된 보정계수에 따라 AADT OD를 보정하여 네트워크 분석의 기초 자료를 구축하였다. 수정된 OD 교통량의 검증을 위하여 기존의 AADT OD의 인구당 통행발생비율과 계절별 평일/주말 OD의 통행발생량을 비교하였다. 그 결과 소수점 두 자리수에서 오차가 발생하여 비교적 합리적인 OD가 추정되었다. 또한 기존의 AADT OD를 이용하여 정책 결정을 할 때의 오류 가능성을 보이기 위하여 각 계절별 평일/주말 OD 교통량과 기존의 AADT OD를 입력 자료로 각각의 네트워크 분석 후 총통행시간의 차이를 분석하였다. 그 결과 정책 결정에 영향을 미칠 수 있을 정도의 차이가 있는 것으로 분석되었다.

• Journal of Korean Society of Transportation
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• v.31 no.2
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• pp.3-10
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• 2013

Two-lane roads that occupy more than a half of rural highways in Korea have operational problems such as traffic congestion problems due to relatively high traffic volume and safety problems due to overtaking risks in two-lane highways. To solve these problems, a 2+1 road pattern that enables to improve traffic safety and operational efficiency of two-lane highways has been applied in Europe. In this study, in-depth applicability of 2+1 roads to Korean rural highways was investigated based on review results of the successful experience of European 2+1 roads. Then, given the Korean two-lane highway conditions, the service traffic volumes for Korean 2+1 roads was estimated. The analysis results showed that 17,000 veh./day might be the maximum traffic volumes for Korean 2+1 roads.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.79-90
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• 2019

This study aims to develop time headway distribution models of bicycle traffic flow in a uninterrupted bikeway. The sample data were collected and classified into two groups of traffic volume levels. The lower level traffic volume is defined to be under 8 bicycles per minute, and the higher one is greater or equal to 8 bicycles per minute. The data aggregation interval size was set to be 0.5-second. Four distribution models including normal distribution, negative exponential distribution, shifted negative exponential distribution, and Pearson III distribution were tested, and Chi-square test results shows that the negative exponential distribution and the shifted negative exponential distribution are well fitted to the sample data. Another test results with different sample data also shows the same conclusion.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.406-414
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• 2015

Existing relative researches for traffic were studied under favorable weather or excluding impact of weather. This study present traffic volume variation according to rainfall intensity in national highway provincial road and rainfall-factor. Continuous traffic count section match AWS after selecting to analyze provincial road 256 section. Weekdays ADT(Average Daily Traffic) and rainfall-factor are influenced by rainfall a little because of business travel. But non-weekdays ADT and rainfall-factor are influenced much more than weekdays because of leisure travel. Estimated AADT(Annual Average Daily Traffic) by adjusting rainfall-factor is lower MAPE than non-adjusting rainfall factor. So, rainfall have to be considered when estimating AADT. ADT decrease according to rainfall intensity, continuous studies considered rainfall intensity are needed when road design and operation.

• Journal of Korean Society of Transportation
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• v.13 no.3
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• pp.99-120
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• 1995

가속차선이 교통류의 운영상태와 안전에 기여하는 바는 벌써부터 인식되어 왔으나 이 변속차선이 유입형 연결로 접속부 전체의 운영에 미치는 영향을 수치화하거나 체계적으로 평가하기 위해 현장 자료를 바탕으로 한 실험적 연구는 진행되어 오지 못하엿다. 현재 널리 참고되고 있는 1985년 USHCM의 접속부 운영상태 분석 방법론은 단지 차선 1의 교통량을 예측하는 데 주안점을 두고 있는데 가속 차선의 길고 짧음에 따라 접속부 바로 전 차선 1의 교통량 분포가 크게 변화한다는 사실(많은 현장 관측을 통해 확인)은 고려하지 못하고 있다. 이는 접속부 운영 상태가 같은 교통량 조건하에서도 크게 차이가 나나다는 것을 뜻하며 가속차선의 존재를 무시한채 운영과 관련한 MOE를 도출하는 것이 서비스수준 산정 방법으로 충분한 것인가 하는 의문을 자연히 낳게 한다. 본 논문은 가속차선이 고속도로 연결로 접속부의 운영에 미치는 영향을 주로 다루고 있다. 가속차선의 독립적인 역할과 영향을 체계적으로 관찰하기 위해 미국내 여러 지역에서 8개의 고속도로 연결로접속부를 선택하고 각 지점에 접속부의 상하류 지역을 포함하는 2,000ft 구간내에 다섯대의 카메라를 설치, 지점별로 약 3시간 동안 자료를 수집하였다. 총 193개 자료수의 분석을 통해서 다중 회귀 모형을 구성하는 독립변수로 가속차선의 길이를 사용하는 것이 타당하다고 결론지었으며, 접속부 운영의 질, 특히 속도를 추정하기 위한 모형을 수립하였다. 본 연구를 통해 얻어진 관점과 방법론은 1994USHCM 고속도로 연결로 분석 방법론 설정에 일부분 반영되고 잇으며 특히 교통운영과 흐름의 방식에서 유사한 엇갈림 구간의 분석 방법과 일관성 있는 분석 체계 마련을 위해서 서비스수준 산정 절차 정립에 엇갈림 알고리즘을 활용하는 방안을 제시하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.13-22
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• 2012

Estimating directional design hourly volume (DDHV) is an important aspect of traffic or road engineering practice. DDHV on highway without permanent traffic counters (PTCs) is usually determined by the annual average daily traffic (AADT) being multiplied by the ratio of DHV to AADT (K factor) and the directional split ratio (D factor) recommended by Korea highway capacity manual (KHCM). However, about the validity of this method has not been clearly proven. The main intent of this study is to develop more accurate and efficient DDHV estimation models for national highway in Korea. DDHV characteristics are investigated using the data from permanent traffic counters (PTCs) on national highways in Korea. A linear relationship between DDHV and AADT was identified. So DDHV estimation models using AADT were developed. The results show that the proposed models outperform the KHCM method with the mean absolute percentage errors (MAPE).