• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.167-175
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• 2010

The ubiquitous transportation system environments make it possible to collect each vehicle's position and velocity data and to perform more sophisticated traffic flow management at the individual vehicle or platoon level through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication. It is necessary to develop a traffic flow management scheme to take advantage of the ubiquitous transportation system environments. This paper proposes an algorithm to advise the optimal speed for each vehicle according to the traffic flow condition. The algorithm aims to stabilize the traffic flow by advising the equilibrium speed to the vehicles speeding or crawling under freely flowing condition. And it aims to prevent or at least alleviate the shockwave propagation by advising the optimal speed that should dampen the speed drop under critical flow conditions. This paper builds a simulation testbed and performs some simulation experiments for the proposed algorithm. The proposed algorithm shows the expected results in terms of travel time reduction and congestion alleviation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

• Wind and Structures
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• v.20 no.2
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• pp.249-274
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• 2015

Long-span cable-stayed bridges exhibit some features which are more critical than typical long span bridges such as geometric and aerodynamic nonlinearities, higher probability of the presence of multiple vehicles on the bridge, and more significant influence of wind loads acting on the ultra high pylon and super long cables. A three-dimensional nonlinear fully-coupled analytical model is developed in this study to improve the dynamic performance prediction of long cable-stayed bridges under combined traffic and wind loads. The modified spectral representation method is introduced to simulate the fluctuating wind field of all the components of the whole bridge simultaneously with high accuracy and efficiency. Then, the aerostatic and aerodynamic wind forces acting on the whole bridge including the bridge deck, pylon, cables and even piers are all derived. The cellular automation method is applied to simulate the stochastic traffic flow which can reflect the real traffic properties on the long span bridge such as lane changing, acceleration, or deceleration. The dynamic interaction between vehicles and the bridge depends on both the geometrical and mechanical relationships between the wheels of vehicles and the contact points on the bridge deck. Nonlinear properties such as geometric nonlinearity and aerodynamic nonlinearity are fully considered. The equations of motion of the coupled wind-traffic-bridge system are derived and solved with a nonlinear separate iteration method which can considerably improve the calculation efficiency. A long cable-stayed bridge, Sutong Bridge across the Yangze River in China, is selected as a numerical example to demonstrate the dynamic interaction of the coupled system. The influences of the whole bridge wind field as well as the geometric and aerodynamic nonlinearities on the responses of the wind-traffic-bridge system are discussed.

• Journal of Communications and Networks
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• v.11 no.3
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• pp.297-305
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• 2009

Multiprotocol label switching (MPLS) networks require dynamic flow admission control to guarantee end-to-end quality of service (QoS) for each Internet protocol (IP) traffic flow. In this paper, we propose to tackle the joint routing and admission control problem for the IP traffic flows in MPLS networks without rerouting already admitted flows. We propose two mathematical programming models for this problem. The first model includes end-to-end delay constraints and the second one, end-to-end packet loss constraints. These end-to-end QoS constraints are imposed not only for the new traffic flow, but also for all already admitted flows in the network. The objective function of both models is to minimize the end-to-end delay for the new flow. Numerical results show that considering end-to-end delay (or packet loss) constraints for all flows has a small impact on the flow blocking rate. Moreover, we reduces significantly the mean end-to-end delay (or the mean packet loss rate) and the proposed approach is able to make its decision within 250 msec.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1169-1176
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• 2000

In this paper, label assignment schemes considering the IP flow model for the efficient MPLS traffic engineering are proposed and evaluated. Based on the IP flow model, the IP flows are classified into transient flows and base flows. Base flows, which last for a long time, transmit data in high bit rate, and be composed of many packets, have good implications for the MPLS traffic engineering, because they usually cause network congestion. To make use of base flows for the MPLS traffic engineering, we propose two base flow classifiers and label assignment schemes where transient flows are allocated to the default LSPs and base flows to explicit LSPs. Proposed schemes are based on the traffic-driven label triggering method combined with a routing tabel. The first base flow classifier uses both flow size in packet counts and routing entries, and the other one, extending the dynamic X/Y flow classifier, is based on a cut-through ratio. Proposed schemes are shown to minimize the number of labels, not degrading the total cut-through ratio.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.169-172
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• 2000

IP Switching is a new routing technology proposed to improve the performance of IP routers. Flow classification is one of the key issues in IP Switching. To achieve better performance, flow classification should be matched to the varying IP traffic and an IP switch should make use of its hardware switching resources as fully as possible. This paper proposes an adaptive flow classification algorithm for IP Switching. By dynamically adjusting the values of its control parameters in response to the present usage of the hardware switching resources, this adaptive algorithm can efficiently match the varying IP traffic and thus improve the performance of an IP switch.

• The KIPS Transactions:PartC
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• v.9C no.1
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• pp.45-54
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• 2002

MPLS(Multiprotocol Label Switching) is regarded as a core technology for migrating to the next generation Internet. In this paper, we propose an dynamic flow admission control supporting DiffServ(Differentiated Services) to provide QoS in MPLS networks. Our proposed model dynamically adjusts the amount of admissible traffic based on transmittable capacity over one outgoing port. It then transmits the Packets while avoiding congested area resulting traffic loss. Ingress LSRs find out the congested area by collecting network state information at QoS state update for QoS routing table. Our Proposed model manages the resource efficiently by protecting the waste of resources that is a critical Problem of DiffServ and makes much more flows enter the network to be served.

• Journal of IKEEE
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• v.22 no.4
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• pp.1099-1103
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• 2018

Platooning of vehicles is an efficient traffic management model that improves traffic flow and fuel consumption. Especially, it is necessary to reduce computational load and networking overhead in automated connected vehicle systems. Because it is important to maintain the size of the platoon group appropriately for efficient platoon operation, this study proposed a dynamic grouping scheme for platooning in an automated vehicle system. The proposed scheme is analyzed by a mathematical model based on Markov chain. From the performance evaluation, it was confirmed that the proposed scheme appropriately controls the size of the platoon group.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.179-188
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• 2017

It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4108-4122
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• 2016

To address challenges of an unprecedented growth in mobile data traffic, the ultra-dense network deployment is a cost efficient solution to off-load the traffic over other small cells. However, the real traffic is often much lower than the peak-hour traffic and certain small cells are superfluous, which will not only introduce extra energy consumption, but also impose extra interference onto the radio environment. In this paper, an elastic energy efficient cell management scheme is proposed based on flow scheduling among multi-layer ultra-dense cells by a SDN controller. A significant power saving was achieved by a cell-level energy manager. The scheme is elastic for energy saving, adaptive to the dynamic traffic distribution in the office or campus environment. In the end, the performance is evaluated and demonstrated. The results show substantial improvements over the conventional method in terms of the number of active BSs, the handover times, and the switches of BSs.