• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.33-42
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• 2020

This study analyzed the bicycle traffic patterns and identified the influence factors for each traffic purpose using the household traffic conditions survey for Seoul. The results are summarized as follows. First, as a result of surveying the bicycle traffic ratios according to the administrative dongs, there was a difference of 14.2% by region. Second, various personal characteristic variables, such as age, gender, income, occupation, and housing type, affect the bicycle mode choice, and bicycle passage increases when using facilities in residential areas. Third, among the neighborhood environments, the bicycle traffic for commuting purposes appeared to increase more in the areas of higher land use mix and lower crime rates. In addition, the bicycle road density and the inclination of the area commonly affect bicycle travel for commuting, shopping, exercising, and leisure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.2 no.2
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• pp.1-10
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• 1982

Presently, most of rail stations are situated on the surface of dowl1town and thus result in heavy traffic congestion and inefficient use of land. This paper analyzes the impact of alternative locations of station On urban traffic patterns by simulating transportation systems, of Busan city. Since location of station has long-term effects on land use and transportation, 20 years forecast of land use change, trip generation, trip distribution, modal split and network assignment was performed for each alternative, and aggregate Impacts On passenger-km and passenger-hour were computed. The result indicated that Bujeon is the most desirable location of station in terms of traffic movement, compared to the alternative locations of Sasang and existing station. Relocation of rail station, however, should be decided with broader analysis including other aspects, such as urban development, environment, construction and operating costs, etc.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.106-113
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• 2019

Mobile traffic is one of the most important indexes of the growth of the mobile communications market, and it has a close relationship with subscribers' service usage patterns, frequency demand and supply, network management, and information communication policy. The purpose of this paper is to understand mobile data usage in Korea and to suggest the optimal steps for establishing the frequency supply and demand system by researching the traffic trends that reflect the characteristics of radio resources in the mobile communications field. To achieve this goal, attempts were made to increase the possibility of policy use by analyzing and forecasting mobile traffic trends, and to improve the accuracy of the research through the verification of the existing prediction results. The paper ends with a discussion of the necessity of a frequency management system based on data science.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.4
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• pp.68-79
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• 2010

Weather like rain, strong wind or snowfall may make the road condition deteriorated and sometimes induce traffic accidents, which lead to severe traffic congestion, thereby travelers may change their destinations elsewhere. Although origin-destination trip information is required to analyze transportation planning in urban area, there are little researches on the relationship between weather condition and travel patterns. This paper investigates the characteristics of travel patterns on expressway in snowing days of 1998-2008. We compare the normal travel patterns with those of snowing days by the travel distance for each vehicle type. Results show that traffic volume and travel distance have been reduced in snowing days as we expect, and also show different travel patterns for weekday and weekend.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.888-896
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• 2023

When an aircraft is landing, it is expected that the aircraft will follow a specified approach procedure and then land at the airport. However, depending on the airport situation, neighbouring aircraft or the instructions of the air traffic controller, there can be a deviation from the specified approach. Detecting aircraft approach patterns is necessary for traffic flow and flight safety, and this paper suggests clustering techniques to identify aircraft patterns in the approach segment. The Gaussian Mixture Model (GMM), one of the machine learning techniques, is used to cluster the trajectories of aircraft, and ADS-B data from aircraft landing at the Gimhae airport in 2019 are used. The aircraft trajectories are clustered on the plane, and a total of 86 approach trajectory patterns are extracted using the centroid value of each cluster. Considering the correlation between the approach procedure pattern and overshoots, the distribution of overshoots is calculated.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.23-33
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• 2004

In this paper, we propose a novel traffic measurement based detection of network attack symptoms on high speed Internet backbone links. In order to do so, we characterize the traffic patterns from the normal and the network attacks appeared on Internet backbone links, and we derive two efficient measures for representing the network attack symptoms at aggregate traffic level. The two measures are the power spectrum and the ratio of packet counts to traffic volume of the aggregate traffic. And, we propose a new methodology to detect networks attack symptoms by measuring those traffic measures. Experimental results show that the proposed scheme can detect the network attack symptoms very exactly and quickly. Unlike existing methods based on Individual packets or flows, since the proposed method is operated on the aggregate traffic level. the computational complexity can be significantly reduced and applicable to high speed Internet backbone links.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.40-49
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• 2011

Integrating massive components and low-power policies have been actively investigated for system-on-chip designs. But in recent years, finding the optimal interconnection structure among heterogeneous components has emerged as a critical system design issue. Therefore, various simulation tools to model interconnection designs are being developed and performance evaluation of simulation is reflected in the real design. But most of the simulation environments employ traffic generation based on the mathematical probability functions, and such traffic generation cannot fully cover for various situations that may be occurred in the real system. Therefore, the demand for traffic pattern generation based on real applications is increasing. However, there have been few simulators that adopt application-specific traffic generators. This paper proposes a novel traffic generation method in simulating various interconnection structures for multi-processor system-on-chip design. The proposed traffic generation method can generate traffic patterns that can reflect the actual characteristics of the application and evaluate the performance of an interconnection structure under more realistic circumstance than traffic patterns using mathematical probability functions. By comparing the differences between the proposed method and the one based on mathematical probability functions, this paper shows advantages of the proposed traffic generation method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• ETRI Journal
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• v.30 no.2
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• pp.290-300
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• 2008

We apply IEEE 802.3 frame burst mode (FBM) to the Ethernet passive optical network (EPON) downstream link and compare its performance with non-frame burst mode for various traffic patterns. Although in light traffic loads (p<0.5) the efficiency of the FBM mechanism is not significant, it does feature high throughput, small jitter, low queue occupancy, and short queuing delay in optical line terminals under various traffic loads with various numbers of optical network units (ONUs). The FBM performance always approaches that of full-duplex mode, especially under heavy traffic loads (p>0.5). Moreover, an increase in number of ONUs will decrease the burst performance. Our work shows that FBM scheme is very useful for EPON transmission and has low design complexity.