• 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.

• 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.

• Journal of Korean Public Health Nursing
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• v.22 no.1
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• pp.84-96
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• 2008

Purpose: To consider how college students' adjustment to college life is related to peer attachment and self-esteem. Method: The subjects were 183 female college students attending one university. The study data were collected with the inventory of peer attachment-revised version, the self-esteem inventory, and the inventory for adjustment to college life. The data were analyzed by t-test, ANOVA, Pearson correlation coefficient and stepwise multiple regression. Results: There were significant differences in self-esteem according to residence type. There were significant differences in the adjustment to college life according to monthly income. There was a significantly positive correlation between peer attachment and adjustment to college life. There were significantly positive correlations between self-esteem and adjustment to college life and all of the subscales of adjustment to college life. Stepwise multiple regression analysis showed that 28.6% of the adjustment to college life was significantly explained by self-esteem and monthly income. Conclusion: Based on these findings, peer attachment is a very important factor influencing self-esteem which is itself a very important factor influencing adjustment to college life in female college students. Therefore, an alternative program designed to increase the self-esteem and peer attachment of female college students should be a planned program based on the study results.

• Journal of Family Resource Management and Policy Review
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• v.3 no.2
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• pp.61-76
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• 1999

The purpose of this study is to investigate the degree of influence on the elderly’s life adjustment and decision making in the family. For these research tasks the data were collected through interview. the respondents were 296 of man and woman elderly who lived in JeonJu. It was analyzed by various statistical methods such as Frequency, Percentile, ANOVA, correlate, t-test, Multiple Regression Analysis. The finding of this study are as follows; 1) Decision-making of the elderly in family had significant differences I the area of sex, the sum of monthly personal expenses, satisfaction level of personal expenses, subjective economic level and self-esteem in the order named. 2) Elderly life-adjustment had significant differences in the area of the residential district, the present job, the situation of a apouse, religion, education, the sum of the monthly personal expenses, health, satisfaction level of personal expenses, subjective economics level and self-esteem. 3) When we observe relationship with the elderly decision-making in family and life-adjustment, relation decision-making in family and life-adjustment appeared positively. 4) Decision-making of the elderly in family had the significant differences according to the variables such as sex, the sum of monthly personal expenses, and self-esteem in the order named, and the most influential factor among them was sex. 5) Elderly life-adjustment had the significant differences according to the variables such as self-esteem, decision-making, religion, health, the sum of the monthly personal expenses, the residential district in the order named. The most influential factor was self-esteem and the explanary of those variables for the elderly life-adjustment was 55.1%.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.2
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• pp.87-92
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• 2002

Soil erosion is detrimental to sustain soil productivity in north Korea, since agriculture of this country depends largely upon the slope land in mountainous area. Taking any measure for protection from erosion should be based on prediction of soil loss. Estimation of rainfall factor, R, in north Korea for the Universal Soil Loss Equation was attempted. The monthly precipitation data of the twenty six locations provided by the Korean Meteorological Adminstration were used. From the relationship between II_30 and the July-August precipitation concentration percents, the regional adjustment factor was obtained. The rainfall factor was calculated with the monthly precipitation data and the regional adjustment factor. The annual precipitation in north Korea ranged from 606 to 1,520mm, and the July-August precipitation concentration percents were 34.4 to 53.8. The regional adjustment factor ranged from 0.53 to 1.33 showing lower value in the highland and east coastal region than in the mid mountainous inland and west region. The R-factor value estimated from the monthly precipitation and the regional adjustment factor ranged from 107 to 483, which was lower than average value in south Korea.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.873-884
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• 2014

The study was conducted to identify the influencing factor of self-esteem and major satisfaction on college adjustment among nursing students. The subjects consisted of 182 nursing students. Data were collected from November to December 2013 and analyzed using SPSS/WIN 21.0 program. The mean score of self-esteem (2.65), major satisfaction (3.43) and college adjustment (3.14) were above the average. Among demographic factors, grade-level, gender, club activities, and monthly income showed significant difference of the score of college adjustment. College adjustment was statistically significantly higher when self-esteem was higher, major satisfaction was higher. Based on the findings of this study, programs promoting major satisfaction and appropriate counseling and academic guidance for nursing students are needed for college nursing students in order to promote college adjustment.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.639-645
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• 2007

In this study, an estimation of call demand for the internet telephony was carried out using the monthly time-series data from June 2001 to December 2004. In the estimation, the call traffic was assumed to be explained by tariff of the internet telephony service, tariff of fixed and wireless services, income, quality of service, and lagged traffic variable. The traffic is assumed to follow the partial adjustment mechanism. The estimation result shows that the call traffic demand is elastic to the tariff of the service while it is inelastic to the change of income. The qualisty of service is regarded as an important factor of demand. Also there appeared the call demand is adjusting to the change of explanatory variables with some lags.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.65-73
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• 2001

Until recently, we use only weekly and monthly adjustment factors in order to estimate the AADT. By the way. we can suppose that the traffic is time series data related to flow of time. So we tried to analyse traffic patterns using time series analysis and apply them to estimate the AADT. We could divide traffic patterns into trend, cyclic variation, seasonal variation and irregular variation like as time series data. Also, in order to reduce random error components, we have looked for the weather conditions as an influential factor. There are many weather conditions such as rainfalls, but, temperatures, and sunshine hours among others but we selected rainfalls and lowest temperatures. And then, we have estimated the AADT using time series factors. To compare the results of, we have applied both irregular variation joined to weather factors and that not joined to. RMSE and U-test were opted at methods to appreciate results of AADT estimation.