• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1B
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• pp.17-24
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• 2008

The effect of the variable packet size on the LRD characteristic of the MMPP traffic model is investigated. When we generate packet traffic for the performance evaluation of IP packet network, MMPP model can be used to generate packet interarrival time. And a random length of packet size from a certain distribution can be assigned to each packet. However, there is a possibility that the variable packet size might change the LRD characteristic of the original MMPP model. In this study, we investigate this possibility. For this purpose the 'refined traffic' is defined, where packet arrival time is generated according to the MMPP model and a random packet length from a specific distribution is assigned to each generated packet. Hurst parameter of the refined traffic is estimated and compared with the original Hurst parameter, which is the input parameter of the MMPP model. We also investigate the effect of the packet size distribution on the queueing performance of the MMPP traffic model and the relationship between the Hurst parameter and queueing performance.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 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.

• Advances in environmental research
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• v.4 no.4
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• pp.211-218
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• 2015

Traffic noise estimation models are useful in evaluation of the noise pollution in current circumstances. They are helpful tools for design and planning new roads and highways. Measurement of average traffic noise level is possible when traffic speed and volume are known. The objective of this study was to devise a model for prediction of highway traffic noise levels based on current traffic variables in Iran. The design of this model was to take the impact of traffic congestion into consideration and to be field tested. This study is a library research augmented by field study conducted on Saeedi Highway located south west of Tehran. The period for the field study lasted 5 days from 7-12 February, 2013. This study examined liner and non-liner methods in formulation of its model. Liner method without a fixed coefficient was the best fit for the intended model. The proposed model can serve as a decision making tool to estimate the impact of key influential factors on sound pressure levels in urban areas in Iran.

• ETRI Journal
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• v.42 no.3
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.75-83
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• 2015

PURPOSES : This study deals with the traffic accidents classified by the traffic analysis zone. The purpose is to develop the accident density models by using zonal traffic and socioeconomic data. METHODS : The traffic accident density models are developed through multiple linear regression analysis. In this study, three multiple linear models were developed. The dependent variable was traffic accident density, which is a measure of the relative distribution of traffic accidents. The independent variables were various traffic and socioeconomic variables. CONCLUSIONS : Three traffic accident density models were developed, and all models were statistically significant. Road length, trip production volume, intersections, van ratio, and number of vehicles per person in the transportation-based model were analyzed to be positive to the accident. Residential and commercial area ratio and transportation vulnerability ratio obtained using the socioeconomic-based model were found to affect the accident. The major arterial road ratio, trip production volume, intersection, van ratio, commercial ratio, and number of companies in the integrated model were also found to be related to the accident.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.3
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• pp.45-59
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• 2011

OFDMA systems have been expected to be widely used to provide multimedia services over wireless channels. To evaluate performance of the OFDMA system, power should be considered as system resource as well as subcarriers. This study propose a queueing traffic model incorporating two kinds of resources (power and subcarriers), and an extended model giving a priority to handoff calls over new calls. Some extensive experiments are conducted to illustrate the usefulness of the proposed traffic model.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.4
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• pp.285-291
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• 2006

A traffic accident is occurred by unbalance of reciprocal action of driver, vehicle and road conditions. To prevent the traffic accident, rapid and perfect road improvement is needed. But most of road improvement plans have insufficient budget. So decision maker has to determine the priority to invest. A model in this study, analyzing the effect of road conditions to the traffic accident, helps to decide the priority in road improvement. This study considered five danger indices ; 1) traffic volume, 2) speed variance, 3) vehicle mixing rate, 4) curved line radius, and 5) difference between design speed and running speed. Danger rate composed by five indices can be a scale of priority of improvement. The model in this study didn't consider all of factors about traffic accident. But this study can propose the methodology for traffic safety policy. For deriving the model, this study used data from highways in Korea and United States. Therefore the model has to apply the highways only.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.169-178
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• 2005

This paper presented high-level function model to achieve traffic engineering to construct traffic engineering infrastructure in Internet. Function model presented include traffic management, capacity management, and network planing. It is ensured that network performance is maximized under all conditions including load shifts and failures by traffic management. It is ensured that the network is designed and provisioned to meet performance objectives for network demands at minimum cost by capacity management. Also it is ensured that node and transport capacity is planned and deployed in advance of forecasted traffic growth by network planning.