• 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.

• 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).

• IE interfaces
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• v.12 no.4
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• pp.625-634
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• 1999

On this study we selected some variable which affect on the estimated of the voice traffic, and estimated daily average traffic by years according to the variables. We applied nonlinear growth curve model to future traffic forecast with estimated historical traffic data. As a result of the forecasting, this study investigates the year in which the internet traffic goes far than the voice traffic.

• 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 Tunnelling and Underground Space Association
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• v.20 no.3
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• pp.561-573
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• 2018

In order to design the ventilation in the road tunnel, it is necessary to know the ratio of average annual daily traffic by vehicle type. In general, the road tunnels are onedirectional tunnel, so the traffic of each vehicle type does not change along the tunnel. On the other hand, in the case of network road tunnels, since the connections in the tunnels are complex, the traffic of vehicle-type varies depending on the network composition of tunnels. In the studying the easy method for calculating the ratio of vehicle type for the network road tunnel are proposed with using incidence matrix.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.203-213
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• 2021

A vehicle crash occurs due to various factors such as the geometry of the road section, traffic, and driver characteristics. A safety performance function has been used in many studies to estimate the relationship between vehicle crash and road factors statistically. And depends on the purpose of the analysis, various characteristic variables have been used. And various characteristic variables have been used in the studies depending on the purpose of analysis. The existing domestic studies generally reflect the average characteristics of the sections by quantifying the traffic volume in macro aggregate units such as the ADT, but this has a limitation that it cannot reflect the real-time changing traffic characteristics. Therefore, the need for research on effective aggregation units that can flexibly reflect the characteristics of the traffic environment arises. In this paper, we develop a safety performance function that can reflect the traffic characteristics in detail with an aggregate unit for one hour in addition to the daily model used in the previous studies. As part of the present study, we also perform a comparison and evaluation between models. The safety performance function for daily and hourly units is developed using a negative binomial regression model with the number of accidents as a dependent variable. In addition, the optimal negative binomial regression model for each of the hourly and daily models was selected, and their prediction performances were compared. The model and evaluation results presented in this paper can be used to determine the risk factors for accidents in the highway section considering the dynamic characteristics. In addition, the model and evaluation results can also be used as the basis for evaluating the availability and transferability of the hourly model.

• 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 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.

• Journal of Environmental Impact Assessment
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• v.10 no.4
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• pp.297-304
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• 2001

Recently the road traffic noise has appeared as a significant environmental issue because of dramatic increase of vehicles and expansion of newly constructed road. Therefore, this study proposes the method that improves prediction factors and models through analysis of the existing road traffic noise prediction model. Prediction factors can be improved by establishing guideline for diffraction attenuation and applying daily traffic discharge, peak traffic discharge, and average traveling speed through an analysis of level service. Prediction must be made by periods of one or five years during 20 years. Prediction models also can be improved to include better prediction model through setting the database, establishing functional relation between physical properties and noise levels by acoustic analysis, and developing models for road traffic noise prediction in residential areas.