• Journal of Korean Society of Transportation
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• 제19권3호
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• pp.133-144
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• 2001

The purpose of this study is to develop micro simulation model for large-scale network with driver's behavior model. This study is performed for uninterrupted flow road section. And this model is developed to simulate traffic flow of the real network with unique geometric structure. The vehicle transmission and drivers' behavior model based on the exiting Cellular Automata approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1866-1883
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• 2019

As the youngest branch of information hiding, network covert timing channels conceal the existence of secret messages by manipulating the timing information of the overt traffic. The popular model-based framework for constructing covert timing channels always utilizes cumulative distribution function (CDF) of the inter-packet delays (IPDs) to modulate secret messages, whereas discards high-order statistics of the IPDs completely. The consequence is the vulnerability to high-order statistical tests, e.g., entropy test. In this study, a rich security model of covert timing channels is established based on IPD chains, which can be used to measure the distortion of multi-order timing statistics of a covert timing channel. To achieve rich security, we propose two types of covert timing channels based on nested lattices. The CDF of the IPDs is used to construct dot-lattice and interval-lattice for quantization, which can ensure the cell density of the lattice consistent with the joint distribution of the IPDs. Furthermore, compensative quantization and guard band strategy are employed to eliminate the regularity and enhance the robustness, respectively. Experimental results on real traffic show that the proposed schemes are rich-secure, and robust to channel interference, whereas some state-of-the-art covert timing channels cannot evade detection under the rich security model.

• International Journal of Highway Engineering
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• 제11권3호
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• pp.61-74
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• 2009

Traffic accident risk index Computation model's development apply traffic level of significance about area of road user group, road and street network area, population group etc.. through numerical formula or model by countermeasure to reduce the occurrence rate of traffic accidents. Is real condition that is taking advantage of risk by tangent section through estimation model and by method to choose improvement way to intersection from outside the country, and is utilizing being applied in part business in domestic. However, question is brought in the accuracy being utilizing changing some to take external model in domestic real condition than individual development of model. Therefore, selection intersection estimation element through traffic accidents occurrence present condition, geometry structure, control way, traffic volume, turning traffic volume etc. in 96 intersections in this research, and select final variable through correlation analysis of abstracted estimation elements. Developed intersection design model taking advantage of signal type, numeric of lane, intersection type, analysis of variance techniques through ANOVA analysis of three variables of intersection form with selected variable lastly, in signal crossing through three class intersection, distinction variable choice risk in model, no-signal crossing risk distinction analysis model and so on develop.

• Journal of Korean Society of Transportation
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• 제22권4호
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• pp.147-158
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• 2004

A class of SWP(Stochastic Wane Propagation) models microscopically mimics individual vehicles' stochastic behavior and traffic jam propagation with simplified car-following models based on CA(Cellular Automata) theory and macroscopically captures dynamic traffic flow relationships based on statistical physics. SWP model, a program-oriented model using both discrete time-space and integer data structure, can simulate a huge road network with high-speed computing time. However, the model has shortcomings to both the capturing of low speed within a jam microscopically and that of the density and back propagation speed of traffic congestion macroscopically because of the generation of spontaneous jam through unrealistic collision avoidance. In this paper, two additional rules are integrated into the NaSch model. The one is SMR(Stopping Maneuver Rule) to mimic vehicles' stopping process more realistically in the tail of traffic jams. the other is LAR(Low Acceleration Rule) for the explanation of low speed characteristics within traffic jams. Therefore, the CA car-following model with the two rules prevents the lockup condition within a heavily traffic density capturing both the stopping maneuver behavior in the tail of traffic jam and the low acceleration behavior within jam microscopically, and generates more various macroscopic traffic flow mechanism than NaSch model's with the explanation of propagation speed and density of traffic jam.

• Journal of Korean Society of Transportation
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• 제21권1호
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• pp.91-102
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• 2003

There are two goals in this paper. The one is development of existing CA(Cellular Automata) model to explain more realistic deceleration process to stop. The other is the application of the updated CA model to forecasting simulation to predict short term link travel time that takes a key rule in finding the shortest path of route guidance system of ITS. Car following theory of CA models don't makes not response to leading vehicle's velocity but gap or distance between leading vehicles and following vehicles. So a following vehicle running at free flow speed must meet steeply sudden deceleration to avoid back collision within unrealistic braking distance. To tackle above unrealistic deceleration rule, “Slow-to-stop” rule is integrated into NaSch model. For application to interrupted traffic flow, this paper applies “Slow-to-stop” rule to both normal traffic light and random traffic light. And vehicle packet method is used to simulate a large-scale network on the desktop. Generally, time series data analysis methods such as neural network, ARIMA, and Kalman filtering are used for short term link travel time prediction that is crucial to find an optimal dynamic shortest path. But those methods have time-lag problems and are hard to capture traffic flow mechanism such as spill over and spill back etc. To address above problems. the CA model built in this study is used for forecasting simulation to predict short term link travel time in Kangnam district network And it's turned out that short term prediction simulation method generates novel results, taking a crack of time lag problems and considering interrupted traffic flow mechanism.

• Journal of Korean Society of Transportation
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• 제34권5호
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• pp.476-488
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• 2016

This study reviewed factors and causes that affect on reliability and accuracy of transportation demand forecasting. In general, the causes of forecasting errors come from variety and irregularity of trip behaviors, data limitation, data aggregation and model simplification. Theoretical understanding about the inevitable errors will be helpful for reasonable decision making for practical transportation policies. The study especially focused on traffic assignment with the KTDB data, and described the factors and causes of errors by classifying six categories such as (1) errors in input data, (2) errors due to spacial aggregation and representation method of network, (3) errors from representing values for variations of traffic patterns, (4) errors from simplification of traffic flow model, and (5) errors from aggregation of route choice behavior.

• The Journal of the Acoustical Society of Korea
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• 제37권6호
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• pp.469-474
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• 2018

As urban population increases, research on urban environmental noise is getting more attention. In this study, we classify the abnormal noise occurring in traffic situation by using a deep learning algorithm which shows high performance in recent environmental noise classification studies. Specifically, we classify the four classes of tire skidding sounds, car crash sounds, car horn sounds, and normal sounds using convolutional neural networks. In addition, we add three environmental noises, including rain, wind and crowd noises, to our training data so that the classification model is more robust in real traffic situation with environmental noises. Experimental results show that the proposed traffic sound classification model achieves better performance than the existing algorithms, particularly under harsh conditions with environmental noises.

• Journal of the Korea Institute of Information Security & Cryptology
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• 제14권4호
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• pp.141-148
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• 2004

This paper proposes an IPSec accelerator performance analysis model based a queue model. It assumes Poison distribution as its input traffic load. The decoding delay is employed as a performance analysis measure. Simulation results based on the proposed model show around 15% differences with respect to actual measurements on field traffic for the BCM5820 accelerator device. The performance analysis model provides with reasonable hardware structure of network servers, and can be used to span design spaces statistically.

• Journal of the Korean Institute of Navigation
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• 제12권1호
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• pp.85-112
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• 1988

From the point of view of safety of life and property at sea and the protection of the marine environment, the Vessel Traffic Management System along the Korea coastal waterway is inevitably introduced. But the establishing priority per area must be evaluated under the restricted budget. In this case, the estimated traffic flow has a major effect on priority evaluation. In the former paper , an algorithm was proposed for estimating the trip distribution between each pair of zones such as harbours and straits. This paper aims to formulate a simulation model for estimating the dynamic traffic flow per area in the Korea coastal waterway. The model consists of the algorithm constrined by the statistical movement of ships and the observed data, the regression analysis and the traffic network evaluations. The processed results of traffic flow except fishing vessel are summarized as follows ; 1) In 2000, the traffic congestions per area are estimated, in proportion of ship's number (tonnage), as Busan area 22.3%(44.5%), Yeosu area 19.8%(11.2%), Wando-Jeju area18.1%(6.8%), Mokpo area 14.9%(9.9%), Gunsan area 9.1%(9.3%), Inchon area 8.1%(7.7%), Pohang area 5.5%(8.5%), and Donghae area 2.2%(2.1%). 2) For example in Busan area, the increment of traffic volume per annum is estimated 4, 102 ships (23 million tons) and the traffic flow in 2000 is evaluated 158, 793 ships (687 million tons). 3) consequently, the increment of traffic volume in Busan area is found the largest and followed by Yeosu, Wando-Jeju area. Also, the traffic flow per area in 2000 has the same order.