• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.155-162
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• 2008

This study shows how to estimate the design hour factor when the counting stations don't have all of the hourly volumes such as in a coverage survey. A coverage survey records traffic volume from 1 to 5 times in a year so it lacks the detailed information to calculate the design hour factor. This study used the traffic volumes of permanent surveys to estimate the design hour factor in coverage surveys using correlation and regression analysis. A total 7 independent variables are used : the coefficient of variance of hourly volume, standard deviation of hourly volume, peak hour volume, AADT, heavy traffic volume proprotion, day time traffic volume proportion and D factor. All of variables are plotted on a curve, so it must use non-linear regression to analyze the data. As a result the coefficient of determination and MAE are good at logarith model using AADT.

• Journal of Urban Science
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• v.7 no.1
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• The Journal of the Acoustical Society of Korea
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• v.9 no.3
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• pp.5-10
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• 1990

This study was examined for traffic noise by platoon dispersion in relation to traffic flow till platoon reach steady driving condition after starting from stopline. Platoon dispersion factor was calculated and evaluated by the following input data : traffic volume, travel time, pass time.

• 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 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).

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.152-161
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• 2022

Recently, it is recognized as a high-cost and inefficient logistics system that increases traffic congestion and environmental problems due to an increase in traffic volume due to the activation of the online market. In order to solve inefficient problems such as unavoidable traffic congestion and environmental problems caused by the increase in traffic volume, it is necessary to develop a freight transport system technology using the existing urban railway infrastructure and freight-only urban railway. The urban subway logistics system is a logistics system that requires a combination of various technologies to solve the nationwide demand for urban logistics and road traffic problems. This paper recognized the existing traffic congestion and environmental pollution of road traffic as problems, and supplemented the contact point requirements presented above by identifying the sub-systems constituting the target system and supplementary points for each part-level contact point. In this study, as a complex system operated for one purpose by grafting various technologies, a plan is required to secure the reliability and safety of operation from various viewpoints. The results of this study can contribute to the initial configuration and basic data to solve the interface bottleneck of the urban subway logistics system to be promoted in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.147-149
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• 2021

The trend of increasing traffic problems due to the explosion in traffic volume in Sejong City has reached a level that cannot be solved by investment in facility infrastructure, so it is essential to establish an intelligent traffic environment based on data. By benchmarking similar cases in the domestic and overseas, and analyzing the traffic information of Sejong City, we propose a plan to provide parking information using Intelligent CCTV, a smart traffic signal control system, and a safe drop zone. It is expected that this study will a basis for establishing policies of the Sejong City traffic safety strategy in the future.

• International Journal of Highway Engineering
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• v.17 no.4
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• pp.135-141
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• 2015

PURPOSES: This study has been conducted to determine a homogeneous segment and integration to improve the efficiency of short-term traffic count. We have also attempted to reduce the traffic monitoring budget. METHODS: Based on the statistical approach, a homogeneous segment in the same road section is determined. Statistical analysis using t-test, mean difference, and correlation coefficient are carried out for 10-year-long (2004-2013) short-term count traffic data and the MAPE of fresh data (2014) are evaluated. The correlation coefficient represents a trend in traffic count, while the mean difference and t-score represent an average traffic count. RESULTS : The statistical analysis suggests that the number of target segments varies with the criteria. The correlation coefficient of more than 30% of the adjacent segment is higher than 0.8. A mean difference of 36.2% and t-score of 19.5% for adjacent segments are below 20% and 2.8, respectively. According to the effectiveness analysis, the integration criteria of the mean difference have a higher effect as compared to the t-score criteria. Thus, the mean difference represents a traffic volume similarity. CONCLUSIONS : The integration of 47 road segments from 882 adjacent road segments indicate 8.87% of MAPE, which is within an acceptable range. It can reduce the traffic monitoring budget and increase the count to improve an accuracy of traffic volume estimation.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.223-237
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• 1996

Based on the traffic and accident data collected on a 4.2km (2.6mile) section of Interstate highway 35W in Minneapolis the relationship between traffic operation variables and safety measures is investigated. An aggregate specification that could be integrated into an urban freeway safety prediction methodology is proposed as a multiple regression model. The specification includes lane occupancy and volume data, which are the control parameters commonly used because they can be measured in real time. The primary variables that appear to affect the safety of urban freeway are : vehicle-miles of travel, entrance ramp volumes and the dynamic effect of queue building. The potential benefits of freeway traffic control strategies on freeway safety are also investigated via a simulation study. It was concluded that improvement of urban freeway safety is achievable by traffic control strategies which homogenize traffic conditions areound critical occupancy values.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.57-67
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• 2000

Two fuzzy travel time estimators for interrupted traffic flow were developed based on field survey data and simulation data 7hat is collected from DETSIM, which is microscopic traffic simulation model that car-following theory is applied. One is FETTOS(Fuzzy Estimator of Travel Time using Occupancy and Spot speed) and the other is FETTOS(Fuzzy Estimator of Travel Speed using Volume and Occupancy). Fuzzy logic controller was applied to the estimators to deal with non-linear relationship between traffic variables and travel time. According to results of simulation and field survey. estimation of travel time can be modeled by using percent occupancy better than any other traffic variables. Detector location from storyline and signal timing Plan of intersection are affected to estimate travel time. With a few findings, the estimator was constructed and its performance was tested for observed travel time data and simulated data. FETTOS which needs signal timing plan and detector location estimates travel time with accurate better than FETSVO does. However. FETSVO has excellent transferability because the estimator needs set of input data only; volume and time mean speed.