• 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.

• 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.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.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• 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.

• 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 Contents Association
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• v.21 no.1
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• pp.277-283
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• 2021

Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.106-118
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• 2020

This study examined the impact of COVID-19 on traffic demand (Average Daily Traffic : ADT) by analyzing the available data on highway traffic volume and the spread of COVID-19 cases in Korea. This study used the data from 228 permanent traffic counts (PTCs) on highways from January to May of 2019 and 2020 to analyze the change in ADT. The first cases of infection in Korea occurred on January 20, 2020, and the maximum daily number of infections was 909 on February 29. On April 30, 2020, the daily number of infections decreased to four. The ADT decreased by 3.3% due to the impact of COVID-19. Considering that the traffic volume has increased 2.3% annually over the past decade, the actual decrease in ADT due to the COVID-19 is estimated to be 5.6% (3.3% + 2.3%). The ADT for weekends decreased significantly, compared to during the week. An analysis of the changes in ADT according to the road type revealed decreases in the following: urban roads -4.6%, rural roads -3.2%, and recreational roads -0.7%. Urban roads decreased the most, and tourist roads decreased the least.

• 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 the Korean Society of School Health
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• v.9 no.1
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• pp.55-67
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• 1996

This research has incorporated a postal survey from the principals, nurse-charging teachers and nurse-teachers of the fiftyfive elementary, middle and high school in Yang-pyeong county area where its supplementary rate of nurse-teachers is less than adequate. It is to analyse the current status of the school health service in the area and to come out with a plan to improve the school health program through the participations of the health related experts of the local community. The survey was done in the two months of period of April to May of 1994. The result of the survey follows. The student population in the Yang-pyeong county area is 13,998 and the school employee population is 904 which counts for about 19.2% of the whole population of the area. However, the supplementary rate of nurse-teachers is only 10.8% (4 in 55 schools) which is very low in terms of relativity. School health committee only exist in 17% of the whole number of schools in the area and 50 of school health committee answered that their activity do not meet the adequate level. Only 54. 3% of the whole school numbers has included the school health finance in their financial plans and the amount set for the school health finance is about 500,000 wons (100,000-1,600,000 wons). 64.9% of the schools in the Yang-pyeong county area have the permanent nursing room established in the school. But, often than the equipment for a simple physical examinations, their supply of the health related equipments are less than adequate. Particitations of school doctor in the school health service is at only 67.6% which pretty much include only the physical examinations. Nurse-charging teachers consider their utmost important role is to teach health education but, they answered that they spent most of their times and efforts on physical examinations & immunizations. The average number of students visition to the nursing room is 2.5 persons and complains for basic discomfort as headache, concussion, stomachache and indigestion problems and usual pills used are the analgesics and digestives. Physical examination is done in the most schools every year but, 51.4% of nurse-charging teachers answered the physical examination does not really help. About the emergency treatment ability, 75.7% reports that both manpower & equipment are short. The school food services are present in only 8 schools (21.6%) but, 89.2% of nurse-charging teachers answer that there is a definite need of the food service. The survey says that the utmost important environmental health and safety factors are the traffic accidents followed by improper heat system, lighting, the stools and desks that do not consider the student physical status The overall evaluation of school health program reports that there are adequate physical examination, immunization, environmental hygiene, and management of safety but, on the other hand, health education, health councelling & management of nursing room are not managed properly. The principals of the survey pool report shortage of public agency support, lack of understanding of school health, shortage of nursing equipments and school health finance as the barrier factors of school health. The nurse-charging teachers report on the same questions as their less than qualitifying expertise, extraload of work upon the nursing affairs, shortage of nursing equipments & school health finance. The head masters & nurse-charging teachers answered that they are desperate for the meetings of nurse-charging teachers, construction of school health councelling system & training education in order to improve school health and if these are available, they will actively participate in them. After the careful analysis of the survey result, it is apparent that through the relations of the manpowers, establishment of community-oriented school health is definitely in need in rural area where there is low supplementary rate of nurse-teachers and poor school health environment.