• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.838-856
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• 2014

Enhanced network speed and the appearance of various applications have recently resulted in the rapid increase of Internet users and the explosive growth of network traffic. Under this circumstance, Internet users are eager to receive reliable and Quality of Service (QoS)-guaranteed services. To provide reliable network services, network managers need to perform control measures involving dropping or blocking each traffic type. To manage a traffic type, it is necessary to rapidly measure and correctly analyze Internet traffic as well as classify network traffic according to applications. Such traffic classification result provides basic information for ensuring service-specific QoS. Several traffic classification methodologies have been introduced; however, there has been no favorable method in achieving optimal performance in terms of accuracy, completeness, and applicability in a real network environment. In this paper, we propose a method to classify Internet traffic as the first step to provide stable network services. We integrate the existing methodologies to compensate their weaknesses and to improve the overall accuracy and completeness of the classification. We prioritize the existing methodologies, which complement each other, in our integrated classification system.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.49-76
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• 1996

Until recently, the inductive loop detecters(ILD) have been used to collect a traffic information in a part of traffic manangment and control. The ILD is able to collect a various traffic data such as a occupancy time and non-occupancy time, traffic volume, etc. The occupancy time of these is very important information for traffic control algorithms, which is required a high accuracy. This accuracy may be improved by classifying a vehicle type with ILD. To classify a vehicle type based on a Analog Digital Converted data collect form ILD, this study used a typical and modifyed statistic method and General Learning Vector Quantization unsuperviser neural network model and a hybrid model of GLVQ and statistic method, As a result, the hybrid model of GLVQ neural network model is superior to the other methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.65-80
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• 2023

In this study, a classification analysis was performed based on the type of traffic situation. The purpose was to derive the major variable factors that could represent the traffic situation. The TTI(Travel Time Index) was used as a criterion for determining traffic conditions, and analysis was performed using data generally detected by the Vehicle Detecting System(VDS). First, the major factors influencing the traffic situation were selected through factor analysis, and traffic conditions were clustered through a cluster analysis of the major factors. After that, variance analysis for each cluster was performed based on the TTI, and similar clusters were merged to categorize the type of traffic situation. The analysis derived, the maximum queue length and occupancy as major factors that could represent the traffic situation. Through this study, it is expected that efficient management of traffic congestion would be possible by just concentrating on the main variable factors that affect the traffic situation.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.46-53
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• 2012

The traffic in research network for the new structure of the network and new service research has the various properties. From the perspective of a specific traffic point of view, transmission of traffic with different requirements using a single routing protocol is an obstacle to satisfy requirements. In this study, we classify the properties of the traffic into two types. We propose the way that we can get the overall optimization effect, the experiment proves it by providing independent multiple forwarding path by applying optimized algorithm by types. In order to distinguish each type of traffic we use the ports on the switch and in order to implement independent path we apply OpenFlow system. In other words, we present the measure that can be implemented to improve the satisfaction of the traffic by making multiple paths by OpenFlow controller according to the type of traffic and by enforcing in Forwarder.

• International Journal of Highway Engineering
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• v.7 no.3 s.25
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• pp.43-52
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• 2005

Highway classification is an essential part of defining design criteria of roads. This study is to classify highways by factor analysis. To accomplish the objectives, factor analysis is performed for classifying highways using the traffic data observed at the permanent traffic count points in 2004. A total off variables are applied : AADT, K factor, D factor, heavy vehicle proportion, day time traffic volume proportion, peak hour volume proportion, sunday factor, vacation factor and COV(Coefficient of Variation). The results of factor analysis show that variables are divided into two factors, which are the factor related to the fluctuational characteristics of traffic volume and the factor related to heavy vehicle and directional volume characteristics. According to the results of cluster analysis, 353 permanent traffic count points are categorized into such three groups as type I for urban highway, type II for rural highway, type III for recreational highway, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.36-56
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• 2022

Traffic accidents occur in Korea are calculated with the 「Automobile Accident Negligence Ratio Certification Standard」 prepared by the 'General Insurance Association of Korea' and the insurance company's agreement or judgment is made. However, disputes are frequently occurring in calculating the negligence ratio. Therefore, it is thought that a more effective response would be possible if accident type according to the standard could be quickly identified using traffic accident information prepared by police. Therefore, this study aims to develop a model that learns the accident information prepared by the police and classifies it to match the accident type in the standard. In particular, through data mining, keywords necessary to classify the accident types of the standard were extracted from the accident data of the police. Then, models were developed to derive the types of accidents by learning the extracted keywords through decision trees and random forest models.

• Journal of Navigation and Port Research
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• v.29 no.10 s.106
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• pp.821-826
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• 2005

The traffic volume will be increased and the traffic of larger vessel could be multiplied in the Pohang port by the opening of the Young-il new port in 2006. Unfortunately, however, potential danger factors to the safe navigation, disordered navigation and traffic congestion are still existing in the Pohang port and approaching waters. This paper describes the status of marine traffic flow and navigational characteristics based on the marine traffic survey using the exclusive software, and the results of marine traffic survey classify into ship's type, size and track history of passing ships through the statistical methods. Finally the examinations of marine traffic route, traffic flow and navigational characteristics are discussed. These results can be used for the best design of ship's routing at the Pohang waters.

• KIEAE Journal
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• v.10 no.5
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• pp.71-76
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• 2010

Recently, the construction and plan of super-tall building is attention link of new town development or urban core regeneration. Super-tall Buildings have many advantages and a lot of affects in urban contexts. Also, construction of super-tall building is will be able to social problem like urban core's decline, loss of openspace, incompatible urban scape, traffic congestion of urban core. But, compares to super-tall buildings affects in urban contexts, there was not extra ordinary study about super-tall building by the urban scale approaches. Therefore, need about study materplan planning of the site which is made to meet super-tall building and urban contexts. There are two main processes in this study. First, to analyze the factors affect to masterplan planning of the super-tall building's site. Through the analyzed factors, classify type of super-tall buildings and identify the type's state. Second, to classify and set the elements of masterplan planning factor in the site. Identify the masterplan planning factor's state by deployment materplan planning factor set the current applied to the constructed super-tall buildings. Through this process, identified the recent trend and providied the basic elements of materplan planning of super-tall building's site.