• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3D
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• pp.205-212
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• 2012

The national highway traffic counts system consists of short duration counts and continuous counts. Unlike continuous counts, short duration counts are performed by collection of a few days period and thus, the magnitude of deviation of collected data from AADT varies depending upon when data collection takes place. Therefore, this study was done to find out the best months and days of data collection of each highway classification in order to enhance the accuracy of AADT estimation. Continuous counts, another type of the national traffic counts system, are performed by collection of 365-day period using a permanent traffic counter. Therefore, it is necessary to keep the number of days for which the counter malfunctions to a minimum in order to enhance the accuracy of data. However, from time to time the permanent traffic counter malfunctions due to various causes and thus, cannot collect data. Therefore, this study was done to find out whether the age of counter, the ratio of heavy vehicle volume to total traffic volume, etc. could be the direct causes of counter's malfunction based on the number of maintenance for a certain time period.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.2009-2019
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• 2013

Traffic volumes are the important basic data which are directly used for transportation network planning, highway design, highway management and so forth. They are collected by two types of collection methods, one of which is the continuous traffic counts and the other is the short duration traffic counts. The continuous traffic counts are conducted for 365 days a year using the permanent traffic counter and the short duration traffic counts are conducted for specific day(s). In case of the continuous traffic counts the missing of data occurs due to breakdown or malfunction of the counter from time to time. Thus, the diverse imputation methods have been developed and applied so far. In this study the applied exponential smoothing method, in which the data from the days before and after the missing day are used, is proposed and compared with other imputation methods. The comparison shows that the applied exponential smoothing method enhances the accuracy of imputation when the coefficient of traffic volume variation is low. In addition, it is verified that the variation of traffic volume at the site is an important factor for the accuracy of imputation. Therefore, it is necessary to apply different imputation methods depending upon site and time to raise the reliability of imputation for missing traffic values.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.21-30
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• 2005

Traffic volume data have been used for the plan, the design, and the operation of highway. Since 1955, traffic survey has been nation- widely carried out at national highway and the regular survey in national highway has been conducted at the intersections of highways. However, it is critical issue to select the priority of the regular survey because it is almost impossible to conduct regular survey at all intersections of national highways. In this study, MCDM(Multiple Criteria Decision Making) using AHP(Analytic Hierarchy Process) was applied to decide the priority of the regular survey. The following standard variables for determining the priority was selected the highway plan variables[AADT, VKT, Peak Hourly Volume, Location of highway from Urban], the highway design variables[Volume(pcu), Directional Traffic Volume, Heavy Vehicle Rate], and the highway operation variables[Speed, Density, V/C]. The standard variables were quantified and normalized. Using the Eigen vector method, the weighted values of each hierarchy based on the pair-wise comparison values from the questionnaire survey were calculated. The selection of the priority of regular survey was dependent on the size of the product of the weighted values for each hierarchy and the normalized values for the standard variables. Finally, the priority of regular survey of the intersections of national highways was determined according to the order in the size of the product of two values.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.921-929
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• 2008

도로절토사면을 지속적으로 관리함에도 불구하고 집중호우 시 도로절토사면 붕괴로 인한 피해가 속출하고 있다. 이러한 절토사면 붕괴에 의한 피해를 최소화 하기위해 각종 신공법들이 개발, 도입되고 있으며 IT와 접목된 상시계측시스템도 많은 현장에 적용되고 있다. 국토해양부 각 지방국토관리청 산하 국도관리사무소에서 관리중인 절토사면 중 현재 상시계측시스템을 운용하고 있는 현장은 총 93개소이며 이들 현장들에 문제가 발생할 시 즉각적으로 경보시스템이 작동하여 붕괴에 의한 피해를 최소한으로 줄일 수 있다. 또한 시스템 작동에 차질이 없도록 정기적인 현장 점검이 이루어지고 있으며 2008년 3월경에 한국건설기술연구원, 한국시설안전공단, (주)GMG에서 합동현장조사를 실시하였다. 이 현장조사 결과를 토대로 전국국도 상시계측시스템의 현황 및 특성, 상시계측시스템의 문제점 및 개선방향에 대하여 분석하였다. 이에 본 연구에서는 전국 국도 사면 중 상시계측 시스템이 설치된 현장에 대하여 암종별, 규모별, 지역별로 분석을 실시하였으며 특히 상시계측시스템이 설치된 현장 중 붕괴발생현장 및 붕괴위험 현장에 대하여 붕괴특성분석, 유지관리 사례분석 등을 실시하였다.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.380-388
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• 2014

Annual average daily traffic(AADT) serves as important basic data in the transportation sector. AADT is used as design traffic which is the basic traffic volume in transportation planning. Despite of its importance, at most locations, AADT is estimated using short term traffic counts. An accurate AADT is calculated through permanent traffic counts at limited locations. This study dealt with estimating AADT using various models considering both the spatial correlation and time series data. Kriging models which are commonly used spatial statistics methods were applied and compared with each model. Additionally the External Universal kriging model, which includes explanatory variables, was used to assure accuracy of AADT estimation. For evaluation of various kriging methods, AADT estimation error, proposed using national highway permanent traffic count data, was analyzed and their performances were compared. The result shows the accuracy enhancement of the AADT estimation.

• 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.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.29-37
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• 2008

In this parer, we purpose for applying the RFID(Radio Frequency IDentification) system in National Road Traffic Volume Survey. Because there is limitation for shipping RFID Tag on every car, we firstly defined Expansion (process of making the number of all cars which passed survey point from sampling data) and determined the best methodology among 3 methodologies (Time factor Model, Fuzzy Model, Artificial Neural Network). As a result of analysis, Time Factor Model was chosen as the best methodology for Expansion. Also, we analyzed to find an application of the RFID system in National Road Traffic Volume Survey and obtained a possibility applying it. It is expected that if the RFID system is used in Traffic Volume Survey, the survey cost is saved than before.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.10-20
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• 2012

This study shows how to estimate AADT(Annual Average Daily Traffic) on temporary count data using new grouping method. This study deals with clustering permanent traffic counts using monthly adjustment factor, daily adjustment factor and a percentage of hourly volume. This study uses a percentage of hourly volume comparing with other studies. Cluster analysis is used and 5 groups is suitable. First, make average of monthly adjustment factor, average of daily adjustment factor, a percentage of hourly volume for each group. Next estimate AADT using 24 hour volume(not holiday) and two adjustment factors. Goodness of fit test is used to find what groups are applicable. MAPE(Mean Absolute Percentage Error) is 8.7% in this method. It is under 1.5% comparing with other method(using adjustment factors in same section). This method is better than other studies because it can apply all temporary counts data.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.131-144
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• 2002

도로의 그룹핑(Grouping)이란 도고 계획, 설계, 관리, 조사 계획 및 정비 방침 등을 세우기 위해 유사한 성격의 도로 구간을 군집화하는 방법이다. 기존에 일반적으로 적용되고 있는 도로 그룹핑 방법은 그룹 수를 미리 지정함으써 분석가의 주관적 판단이 개입되었고, 그룹핑 변수 선정에 대한 근거가 부족하였다. 이에 본 연구에서는 기존에 일반적으로 적용되고 있는 도로 그룹핑 방법을 개선하여 새로운 방법론을 제시하였다. 또한 새로 제시된 방법론의 검증을 위해 도로 교통량 통계연보에서 제공하고 있는 일반국도의 2000년 294개 상시조사 지전의 교통량 자료를 이용하여 분석하였다. 연구 결과 기존의 월, 요일 변동계수만을 적용한 그룹핑 방법보다는 기타 교통지표(AADT, $\Sigma$K1000(K값의 상위 1000번 순위까지의 누적 값), 중차량 비율, 주야율)를 동시에 적용할 때 좀 더 효율적이면서 세부적으로 분류됨을 알 수 있었다. 또한 기타 교통지표론 적당한 그룹핑 결과로는 5그룹의 국도 기능 분류가 가능함을 알 수 있었다. 그 결과 기존의 소재지역과 기능에 따른 국토의 구분을 지방 산업도로 그룹, 지역 간선도로 그룹, 대도시 주변형 도로 그룹, 중소도시 주변형 및 관광도로 그룹, 관광도로 그룹으로 분류할 수 있었다. 본 연구에서의 도로 그룹핑 결과에 각 지역특성을 추가하여 분석한다면 도로의 계획, 선계, 관리 등에 매우 유용한 자료로 활용되리라 예상한다. 또한 본 연구의 결과를 이용하면 좀 더 효율적으로 설계시간계수 선정, 전역 조사 지점의 AADT추정, 상시 교통량 조사 자료의 누락 데이터 보정 및 교통량 조사의 스케줄링에 많이 활용할 수 있을 것으로 기대된다.