• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.381-388
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• 2018

This study proposes marine traffic volume estimation method considering ship speed, a factor excluded from the existing method. Ten days of GICOMS marine traffic data from Pyeongtaek and Dangjin ports was applied to this study. As a result, converted traffic volume with the proposed estimation method showed an increase of 4.41 (${\pm}0.99$) times or decrease of 0.59 (${\pm}0.04$) at most, compared with the existing estimation method. Average marine traffic congestion for each time applying the proposed estimation method showed an increase of 1.43 (${\pm}0.10$) compared with the existing estimation method. The maximum marine traffic congestion for each time was 1.62 (${\pm}0.34$) times higher compared with the existing estimation method. Marine traffic peak time, defined as the highest point of marine traffic congestion, was evaluated to be different from that of the existing method because of distribution of vessel speed. In conclusion, considering ship speed is necessary when estimating marine traffic volume to produce a practical estimate of marine traffic capacity.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.3
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• pp.273-278
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• 1995

This study is to estimate the ventilation volume by the traffic that originated from driving automobiles for two tunnels (Kugi tunnel and Kumhwa tunnel) that adopted natural ventilation system among tunnels of Seoul, and on the basis of which, we estimated the ventilation velume at various conditions. With the result of the estimation, we will present the basic method that can be operated with the optimum condition for the ventilation system. Estimating the predicted ventilation volume in the tennel by the pollutant concentration, we used traffic volume and CO emission data by the automobile speed and CO concentration in the tunnel. And, when we estimated the traffic ventilation volume by natural and traffic ventilation force, we used traffic volume, automobile speed, tunnel area, automobile area data and so on. As the result of simple regression between predicted ventilation volume and traffic ventilation volume, we attained the regression coefficient 0.88, and achieved the relation form that predicted ventilation volume equal 0.12x traffic ventilation volume-92, 000. Using this equation, we estimated the ventilation volume to satisfy the enviromnental standards of several space, and calculated the required volume for mechanical ventilation. Incase of Kumhwa Tunnel, there is a need of mechanical ventilation all day long to satisfy air quality standard 9 ppm for 8 hours average and 10 ppm for the indoor air quality standard of public facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.1-13
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• 2023

This study develops an optimal mobile-communication-based National Highway traffic volume estimation model using an ensemble-based machine learning algorithm. Based on information such as mobile communication data and VDS data, the LightGBM model was selected as the optimal model for estimating traffic volume. As a result of evaluating traffic volume estimation performance from 96 points where VDS was installed, MAPE was 8.49 (accuracy 91.51%). On the roads where VDS was not installed, traffic estimation accuracy was 92.6%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Advances in environmental research
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• v.4 no.4
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• pp.211-218
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• 2015

Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.115-124
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• 2010

VDF(volume-delay function) is one of the most important factor to improve the reliability of traffic demand estimation because it is for estimation of link travel time based on the traffic volume variation. Because VDF of link except for freeway is applied as the parameter of BPR(bureau of public road) of U.S., it causes to deteriorate the accuracy of traffic demand estimation. The purpose of this paper is to establish new parameter of VDF based on the real-surveyed traffic data in order to improve the problem of the existing VDF. We suggest the reclassification of road hierarchy, the approach of traffic survey, the estimating method of VDF parameter, and the improvements of new VDF application. The new VDF allows us to estimate more realistic traffic situation in parts of demand, travel time and path between origin-destination.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.120-130
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• 2019

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.

• 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.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.447-454
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• 2007

To assess the port development and maritime traffic environment, the future traffic volume has been estimated using the number of inbound and outbound vessel for a specific port. The estimation of future traffic volume should be considered as an important factor to establish the degree of fairway congestion, the determination of fairway width and the operational role. Until now, the number of in and out vessel for the port has been only estimated mainly, but the type and size of inbound and outbound ships are different depending on the port's characteristics. So, it is difficult to estimate the future traffic volume using the change of only one item. This paper calculates the future traffic volume using the marine traffic characteristic factors as the number of coastal ship and ocean-going ship, the size of ship and the change of cargo volume per a ship etc. And it compared with the results of Artificial Neural Network(ANN) for accurate identification of nonlinear system.

• 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).