• Journal of KIISE
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• v.44 no.4
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• pp.433-438
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• 2017

Presently, the ubiquitous emergence of high-speed-network environments has led to a rapid increase of various applications, leading to constantly complicated network traffic. To manage networks efficiently, the traffic classification of specific units is essential. While various traffic-classification methods have been studied, a methods for the complete classification of network traffic has not yet been developed. In this paper, a correlation model of the network flow is defined, and a traffic-classification method for which this model is used is proposed. The proposed network-correlation model for traffic classification consists of a similarity model and a connectivity model. Suggestion for the effectiveness of the proposed method is demonstrated in terms of accuracy and completeness through experiments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.103-113
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• 2013

Shock wave model describes the propagation speed of kinematic waves in traffic flow. It was first presented by Lighthill and Whitham and has been deployed to solve many traffic problems. A recent paper pointed out that there are some traffic situations in which shock waves are not observable in the field, whereas the model predicts the existence of waves. The paper attempted to identify how such a counterintuitive conclusion results from the L-W model, and resolved the problem by deriving a new asymptotical shock wave model. Although the asymptotical model successfully eliminated the paradox of the L-W model, the validation of the new model is confined within the realm of the deceleration flow situation since the model was derived under such constraint. The purpose of this paper is to derive the remaining counter asymptotical shock wave model for acceleration traffic flow. For this, the vehicle trajectories in a time-space diagram modified to accommodate the continuously increased speed at every instant in such a way that the relationship between the spacing from the preceding vehicle and the speed of the following vehicle strictly follows Greenshield's model. To verify the validity of the suggested model, it was initially implemented to a constant flow where no shock wave exists, and the results showed that there exists no imaginary shock wave in a homogeneous flow. Numerical applications of the new model showed that the shock wave speeds of the asymptotical model for the acceleration flow tend to lean far toward the forward direction consistently. This means that the asymptotical models performs in a systematically different way for acceleration and for declaration flows. Since the output difference among the models is so distinct and systematic, further study on identifying which model is more applicable to an empirical site is recommended.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.63-80
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• 2007

This study is a generalization of 'stable dynamics' recently suggested by Nesterov and de Palma[29]. Stable dynamics is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with user equilibrium model that is common in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on the congestion. Therefore it is expected to be an useful analysis tool for transportation planners. An equilibrium in stable dynamics needs only maximum flow in each arc and Wardrop[33] Principle. In this study, we generalize the stable dynamics into the model with multiple traffic classes. We classify the traffic into the types of vehicle such as cars, buses and trucks. Driving behaviors classified by age, sex and income-level can also be classes. We develop an equilibrium with multiple traffic classes. We can find the equilibrium by solving the well-known network problem, multicommodity minimum cost network flow problem.

• Journal of Communications and Networks
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• v.14 no.3
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Journal of Korean Society of Transportation
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• v.5 no.2
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• pp.73-80
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• 1987

Various procedures for evaluation of traffic noise annoyance have been proposed. However, most of the studies of this type are restricted for improving traffic flow. In this paper, a method to predict the road traffic noise is proposed in terms of equivalent continuous A-Weighted sound pressure level (Leq), based on a probability model. First, distribution of the road traffic noise level are investigated. second, the weibull distribution parameters are estimated by using the quantification theory. Finally, a prediction model of the road traffic noise is proposed based on the weibull distribution model The predicted values of the Leq are closely matched the measured data.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.27-34
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• 2009

The operational effect of traffic signal control improvement was evaluated using the Two-Fluid Model. The parameters engaged in the Two-Fluid Model becomes food indicators to measure the quality of traffic flow due to the improvement of traffic signal operation. A series of experiment were conduced for the 31 signalized intersections in Uijeongbu City. To estimate the parameters in the Two-Fluid Model the trajectory informations of individual vehicles were collected using the CORSIM and Run Time Extension. The test results showed 35 percent decrease of average minimum trip time per unit distance. One of the parameters in the Two-Fluid Model is a measure of the resistance of the network to the degraded operation with the increased demand. The test result showed 28 percent decrease of this parameter. In spite of the simulation results of the arterial flow, it was concluded that the Two-Fluid Model is useful tool to evaluate the improvement of the traffic signal control system from the macroscopic aspect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.58-64
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• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.3
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• pp.145-159
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• 1985

An understanding of interrelationships among basic characteristics of vehicular traffic flow, such as volum, speed, headtime, and density, is of prime importance. Similarly in providing proper level of servicebility in the field of base of design and traffic control, it is deeply connected. After all, with a view to improve traffic flow characteristics, future efforts about the mutual function development between rod and traffic should be made on the basis of present traffic characteristics. This paper figures out some traffic characteristics from field data and provides proper model of equation to estimate traffic volume on the road.