• 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.

• Journal of Korean Society of Transportation
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• v.1 no.1
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• pp.56-63
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• 1983

Intersection control has dual-purposes; increasing capacity and reducing delay. The primary concern of efficient intersection control under oversaturated condition as in Korea is to increase capacity. Prevailing intersection operation technique permits thru traffic to utilize left turn lane, because the intersection without left turn pocket has left turn signal interval. In this situation, it seems not to be valid to calculate capacity, delay, and signal timings by conventional methods. By critical lane technique, capacity increases as cycle length increases. However, when thru traffic utilize LT lane, the capacity varies according to LT volume, LT interval as well as cycle length, which implies that specific cycle length and LT interval exist to maximize capacity for given LT volume. The study is designed is designed to calculate utilization factors of LT lane for thru traffic and capacities, and identify signal timings to yield maximum capacity. The experimental design involved has 3 variables; 1)LT volumes at each approach(20-300 vph), 2)cycle lengths (60-220 sec), and 3)LT intervals(2.6-42 sec) for one scenario of isolated intersection crossing two 6-lanes streets. For LT volume of 50-150 vph, capacity calculated by using the utilization factor is about 25% higher than that by critical lane method. The range of optimum cycle length to yield maximum capapcity for LT volume less than 120 vph is 140-180 sec, and increases as LT volume increases. The optimum LT interval to yield maximum capacity is longer than the intrval necessary to accommodate LT volume at saturation flow rate.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.79-89
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• 2012

Noise pollution on the residential areas adjacent to urban expressway is a very severe situation. There has been a significant increases in the number of residential areas near urban expressway, and the quality of life in such areas is being degraded due to increased traffic volume and speed. In this study, several factors such as traffic volume, speed, and distance from noise source, etc. that affect the noise level were investigated for the residential areas near urban expressway. A correlation and regression model was built to examine the relationship between such traffic factors and noise. The study result found that traffic volume had a closer relationship with noise than speed had. Therefore, to decrease road traffic noise, it is more imperative to control traffic volume than traffic speed.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.109-114
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• 2016

PURPOSES : The demand for extending national highways is increasing, but traffic monitoring is hindered because of resource limitations. Hence, this study classified highway segments into 5 types to improve the efficiency of short-term traffic count planning. METHODS : The traffic volume trends of 880 highway segments were classified through R-squared and linear regression analyses; the steadiness of traffic volume trends was evaluated through coefficient of variance (COV), and the normality of the data were determined through the Shapiro-Wilk W-test. RESULTS : Of the 880 segments, 574 segments had relatively low COV and were classified as type 1 segments, and 123 and 64 segments with increasing and decreasing traffic volume trends were classified as type 2 and type 3 segments, respectively; 80 segments that failed the normality test were classified as type 4, and the remaining 39 were classified as type 5 segments. CONCLUSIONS : A theoretical basis for biennial count planning was established. Biennial count is recommended for types 1~4 because their mean absolute percentage errors (MAPEs) are approximately 10%. For type 5 (MAPE =19.26%), the conventional annual count can be continued. The results of this analysis can reduce the traffic monitoring budget.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.6
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• pp.1059-1068
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• 2016

The current operation method for the AVC (Automatic Vehicle Classification) equipment does not generate vehicle speed, traffic volume and vehicle type information when part of the sensors has failed. Inefficiency of current methods would not use the collected data from the normal sensor. In this study was conducted research on the calculating method at the traffic volume and vehicle speed in the sensor failure AVC equipment. The failure situation of the sensor was classified into 4 types. Calculating the traffic volume and vehicle speed information for each type, and accuracy of these informations were analyzed. Analysis results, traffic volume was possible to calculate a highly accurate value (accuracy: 100%, 98%, 97%). In the case of speed, the accuracy of the calculated speed value reaches a level that can be accepted sufficiently (RMSE value is less than 16.8). So, using the methodology proposed in this study are expected to be able to increase the operational efficiency of the AVC equipment.

• Journal of the Korean Institute of Navigation
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• v.21 no.2
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• pp.19-40
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• 1997

When designing new marine traffic routes, it is desirable that the congestion due to traffic volume is evaluated by theoretical traffic capacity or by traffic simulation. Most of these techniques are applied to single server which is not considered channel width. Over-taking or paralle sailing of two or more vessels is allowalbe in Dover, Uraga, Gaduk-sudo, etc under their traffic capacity. In this paper, the Bumper Model is introduced to multiple severs in narrow channel and applied to Uraga Channel in Japan. The minimum width of Uraga Channel is 1, 400 m and its design traffic capacity is evaluated 19.26 ~ 19.52% of the basic traffic capacity. The traffic capacity on Gaduk Channel according to Busan New Port Development in 2011 will be estimated 3.59 % of maximum density and equal to 18.6% of that on Uraga Channel in 1992. The channel width Gaduk-sudo is designed 1, 600~2, 460 m and evaluated safe enough.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.584-590
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• 2000

Urban traffic situations are extremely complex and highly interactive. The multi-agent systems approach can provide a new desirable solution. Currently, a traffic simulator is needed to understand and explore the difficulties in an agent-oriented traffic control. This paper presents an agent-oriented fuzzy logic controller for multiple crossroads simulation. A fuzzy logic control simulation with variables of arrival, queue, and traffic volume could alleviate traffic congestion. We developed an agent-oriented simulator suitable for traffic junctions with η$\times$η intersections in Visual C++. The proposed method adaptively controls the cycle of traffic signals even though the traffic volume varies. The effectiveness of this method was shown through simulation of multiple intersections.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.78-78
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• 2017

• 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%.