• 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).

• 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.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.

• Journal of the Korean Professional Engineers Association
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• v.42 no.4
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• pp.45-50
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• 2009

The modal split structure of the Korea's transportation system has been dominated by road-oriented structure. The shortage of the inftrastructure to accommodate the rapidly increasing travel demand has brought about socio-economic losses such as severe traffic congestion and high logistic costs, and thereby weakened the competitiveness of the country. Highway transportation sector is more vulnerable to energy consumption comparing with railway sector since the highway sector is dependent mostly on fossil fuels for its energy source. In 2006 annual road cogestion costs in Korea reached 24.6 trillion won, with an average annual growth rate of 5.4%. The annual road congestion cost of intercity highways were 9.2 trillion won. As the new cities that recently developed are located far from Seoul area, the boundary of commuting in Seoul metropolitan area is extended. It makes passengers have longer trips with longer travel time, and the congestion problem to be more serious. In this regards, Gyeonggi Provincial Government proposed a deeper underground metropolitan express train system for the greater Metropolitan area. which is named as GTX. Gyeonggi Province suggested 3 key underground lines, based on the outcome of the feasibility study conducted by the Korea Society of Transportation, and submitted to the Ministry of Land Transportation and Maritime Affairs for its review. If the project is approved for construction and completed in 2016, the daily volume of surface traffic bound for Seoul will be reduced substantially and therefore the users will be benefitted for time savings by an annual amount of 2 trilion won every year.

• 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.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.542-553
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• 2018

Since many previous studies reported the health effect of air pollution and indicated traffic as a major pollution source, significant policy efforts have been made to control traffic to reduce air pollution. However, there have been few studies that evaluated such policy implementation. In Seoul, Korea, the exclusive median bus lane system was implemented in 2004, and the metropolitan government applied air pollution reduction policies such as conversion of diesel buses to compressed natural gas buses and installation of emission control devices. This paper aimed to investigate the impact of the exclusive median bus lane system on air pollution reduction. Using hourly concentrations of particulate matter ($PM_{10}$) and nitrogen dioxide ($NO_2$) measured at 131 regulatory monitoring sites in Seoul and Gyeonggi-do for 2001-2014, we calculated annual and daily average concentrations at each site. We assessed the impact of the policy using differences-in-differences analysis by annual and daily average models after adjusting for geographic and/or meteorological variables. This method divides population into treatment and control groups with and without policy application, and compares the difference between the two time periods before and after the policy implementation in the treatment group with the difference in the control group. We classified all monitoring sites into treatment and control groups using two definitions: 1) Seoul vs. Gyeonggi-do; 2) within vs. outside 300 meters from the median bus lane. Pre- and post-policy periods were defined as 2001-2005 and 2006-2014, and 2004 and 2014 in the annual and daily models, respectively. The decrease in $PM_{10}$ concentrations between the two periods across monitoring sites in the treatment group was larger by $1.73-5.88{\mu}g/m^3$ than in the control group. $NO_2$ also showed the decrease without statistical significance. Our findings suggest that an efficient public transport policy combined with pollution abatement policies can contribute to reduction in air pollution.