• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.559-565
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• 2016

For the purpose of the research, it is analyze the influence traffic Signal Progression on a CBD by expansion of Permissive Left-Turn on the advanced traffic management system program compared with traffic response control For this, divided a average distance between intersections, a traffic network on five district in four city. As the result, Volume of the traffic management system on a advanced traffic management system program compared with traffic response control is effective in traffic signal cycle 160sec 286car per lane, in 140sec 308car, 120sec 264car. As well, for a traffic network as the length of a traffic network 2.0~3.0km, under 2.0km, all of traffic signal cycle(160sec, 140sec, 120sec) a passing band and stop is more effective. But the traffic management system on traffic response control compared with the traffic management system on a advanced traffic management system program is effective in the length of a traffic network over 5.0km. For the efficiency of traffic signal system manage, it should be runned the traffic management system on traffic response control in addition to the traffic management system on a advanced traffic management system program on CBD. As the result of simulation the business in chungju-si, the travel speed of the traffic management system on a advanced traffic management system program is 41.2km/h and the travel speed of traffic response control is 37.5km/h. Therefore, it should be runned per length of a traffic network the traffic management system on traffic response control in addition to the traffic management system on a advanced traffic management system program on CBD.

• ETRI Journal
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• v.42 no.3
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• pp.311-323
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• 2020

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet's sizes, packet's inter-arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.

• Ocean Systems Engineering
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• v.12 no.3
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• pp.267-284
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• 2022

A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.679-690
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• 2011

In this paper we consider a local area network (LAN) of dual mode service where one is a token bus and the other is a carrier sense multiple access with a collision detection (CSMA/CD) bus. The objective of the paper is to find the overall cell/packet dropping probability of a dual mode LAN for finite length queue M/G/1(m) traffic. Here, the offered traffic of the LAN is taken to be the equivalent carried traffic of a one-millisecond delay. The concept of a tabular solution for two-dimensional Poisson's traffic of circuit switching is adapted here to find the cell dropping probability of the dual mode packet service. Although the work is done for the traffic of similar bandwidth, it can be extended for the case of a dissimilar bandwidth of a circuit switched network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.313-329
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• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.664-667
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• 2003

We propose a frizzy traffic controller of Sugeno's fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It uses a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. In order to construct fuzzy traffic controller of Sugeno's fuzzy model we first model the control process of the traffic light by using Mamdani's fuzzy model, which has the uniform membership functions of the same size and shape. Next we make Mamdani's fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Lastly, we construct the fuzzy traffic controller of Sugeno's fuzzy model by learning from the input/output data, which is retrieved from Mamdani's fuzzy model with the non-uniform membership functions. We compared and analyzed the service level of the traffic light controllers by using the delay time. As a result of comparison, the fuzzy traffic controller of Sugeno's fuzzy model shows the best service level of them.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Journal of the Korean Society of Systems Engineering
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• v.3 no.1
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• pp.27-31
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• 2007

A complex system like ATM(Air Traffic Management) has safety problem emerging from complex interactions between systems. In complex systems, malfunctions of components are not the only causes of critical accidents. To resolve this problem many researchers have proposed new safety analysis models for complex systems. This research is a way of improving safety analysis model focusing on systems engineering design model for ATM.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.239-244
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• 2007

In the telematics system, a reasonable path search time should be guaranteed from a great number of user's queries, even though the optimal path with minimized travel time might be continuously changed by the traffic flows. Thus, the path search method should consider traffic flows of the roads and the search time as well. However, the existing path search methods are not able to cope efficiently with the change of the traffic flows and to search rapidly paths simultaneously. This paper proposes a new path search method for fast computation. It also reflects the traffic flows efficiently. Especially, in order to simplify the computation of variable heuristic values, it employs a simplification method for estimating values of traffic-adaptable heuristics. The experiments are carried out with the $A^*$ algorithm and the proposed method in terms of the execution time, the number of node accesses and the accuracy. The results obtained from the experiments show that the method achieves very fast execution time and the reasonable accuracy as well.