• 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.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).

• 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 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.

• 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.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.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.83-95
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• 2017

PURPOSES : The purpose of this study is to develop a regression model to predict the International Roughness Index(IRI) and Surface Distress(SD) for the estimation of HPCI using Expressway Pavement Management System(PMS). METHODS : To develop an HPCI prediction model, prediction models of IRI and SD were developed in advance. The independent variables considered in the models were pavement age, Annual Average Daily Traffic Volume(AADT), the amount of deicing salt used, the severity of Alkali Silica Reaction(ASR), average temperature, annual temperature difference, number of days of precipitation, number of days of snowfall, number of days below zero temperature, and so on. RESULTS : The present IRI, age, AADT, annual temperature differential, number of days of precipitation and ASR severity were chosen as independent variables for the IRI prediction model. In addition, the present IRI, present SD, amount of deicing chemical used, and annual temperature differential were chosen as independent variables for the SD prediction model. CONCLUSIONS : The models for predicting IRI and SD were developed. The predicted HPCI can be calculated from the HPCI equation using the predicted IRI and SD.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.11-16
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• 2009

The estimation of total travel time on highway link for a day or year is the most important process for the feasibility analysis of highway or railway. Most of current guidelines for feasibility studies have been based on the time-traffic volume relationship from the BPR, and the traffic volumes have been determined by the application of the design hour factor to the annual average daily traffic volume. Both of the BPR function and the application of the design hour volume may result in the over-estimation of travel time due to the fact that the traffic volume on the large portion of highway links in Korea are close to the capacities. This study proposed a new way which is based on the distribution of hourly volumes for a year. It could be closer to the real situation, and provide more reasonable estimation. This methodology was validated for the national highways, but may be applicable for any type of highway with the AADT.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• 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.