• International Journal of Highway Engineering
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• v.15 no.3
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.3
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• pp.71-86
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• 2013

This research explores the question how interactions between customer and firm affect firm growth. To test suggested hypotheses, this study collects data on social commerce industry in Korea during the period from the beginning of social commerce industry in Korea, May 2010, to March 2012, and investigates the effect of social media on the growth of social commerce firms. We suggest two hypotheses in this study. First, as web traffic inflow through social media into a focal social commerce increases, the growth rate of the focal social commerce increases. Second, the more diverse social media channel through which web traffic inflows into a focal social commerce, the weaker the positive effect of web traffic inflow on the growth rate of the focal social commerce. Analysis of data shows that inflow through social media is positively related to the growth of social commerce. In addition, our analysis shows that inflow channel diversity weakens the positive relationship between web traffic inflow through social media and growth rate of social commerce firms. These results suggest that firms need to concentrate on few social media in order to attract customers. The study contributes to understanding how interaction between firms and customers influences the growth of the firm.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• Horticultural Science & Technology
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• v.33 no.3
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• pp.340-348
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• 2015

Mowing turfgrasses, especially fast growing species like perennial ryegrass, is one of the most time and money consuming tasks of their management. Trinexapac-ethyl (TE) is a popular plant growth regulator used to reduce mowing requirements, improve stress tolerance, and enhance turf quality. This study was conducted to investigate the effect of TE rate and frequency of applications on growth response and traffic tolerance of perennial ryegrass. The experiment was a split-plot laid out in a randomized complete block (RCB) design with three replications. TE was applied to main plots at 0.00, 0.25, and $0.50kg\;a.i.\;ha^{-1}$. Application pattern included an initial application, followed by two sequential applications at 6-wk intervals. Traffic treatment was applied to subplots with a cleated roller. Results demonstrated that TE consistently reduced vertical shoot growth, clippings dry weight, with maximum growth reduction of 59% and 65%, for 0.25 and $0.50kg\;a.i.\;ha^{-1}$, respectively, occurring at 2 weeks after initial TE treatment (WAT). Traffic also dramatically reduced vertical shoot growth and clippings dry weight. Overall, quality of perennial ryegrass was enhanced by sequential TE applications, however, turf quality and surface coverage reduced greatly under traffic, regardless of TE treatment. Total chlorophyll, chlorophyll a and chlorophyll b and total carbohydrates (TC) contents were also positively influenced following sequential TE application. Our results indicated that TE reduces mowing frequency and enhances turf quality rather than influencing traffic resistance.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.127-131
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• 2011

Even if there are no causing factors such as car crash and road works, traffic congestion come from traffic growth on the road. In this case, estimation of traffic flow helps find the solution of traffic congestion problem. In this paper, we present a optimization model which used on traffic equilibrium problem and studied the problem of inverting shortest path sets for complex traffic system. And we also develop pivotal decomposition algorithm for reliability function of complex traffic system. Several examples are illustrated.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.531-543
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• 2023

The existing concrete bridges are time-varying working systems, where the maintenance strategy should be planned according to the time-varying performance of the bridge. This work proposes a time-dependent residual capacity assessment procedure, which considers the non-stationary bridge load effects under growing traffic and non-stationary structural deterioration owing to material degradations. Lifetime bridge load effects under traffic growth are predicated by the non-stationary peaks-over-threshold (POT) method using time-dependent generalized Pareto distribution (GPD) models. The non-stationary structural resistance owing to material degradation is modeled by incorporating the Gamma deterioration process and field inspection data. A three-span continuous box-girder bridge is illustrated as an example to demonstrate the application of the proposed procedure, and the time-varying reliability indexes of the bridge girder are calculated. The accuracy of the proposed non-stationary POT method is verified through numerical examples, where the shape parameter of the time-varying GPD model is constant but the threshold and scale parameters are polynomial functions increasing with time. The case study illustrates that the residual flexural capacities show a degradation trend from a slow decrease to an accelerated decrease under traffic growth and material degradation. The reliability index for the mid-span cross-section reduces from 4.91 to 4.55 after being in service for 100 years, and the value is from 4.96 to 4.75 for the mid-support cross-section. The studied bridge shows no safety risk under traffic growth and structural deterioration owing to its high design safety reserve. However, applying the proposed numerical approach to analyze the degradation of residual bearing capacity for bridge structures with low safety reserves is of great significance for management and maintenance.

• IE interfaces
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• v.12 no.4
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• pp.625-634
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• 1999

On this study we selected some variable which affect on the estimated of the voice traffic, and estimated daily average traffic by years according to the variables. We applied nonlinear growth curve model to future traffic forecast with estimated historical traffic data. As a result of the forecasting, this study investigates the year in which the internet traffic goes far than the voice traffic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.254-264
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• 2019

This study is a descriptive correlation study investigating the effects of stress disorder symptoms, resilience, and social network on post-traumatic growth in traffic accident patients. The participants were 158 traffic accident cases enrolled from five 100-bed hospitals situated in city C. Data were collected from July 1 to August 31, 2018, and analyzed by t-test, ANOVA, Pearson's correlation coefficients, and multiple regression using SPSS / Win23. The explanatory power of post-traumatic growth was determined to be 36.9%, and the factors affecting post-traumatic growth were social network and post-traumatic stress disorder. In addition, social networks completely established the relationship between resilience and post-traumatic growth. Our results confirmed that a wider social network and increased symptoms of post-traumatic stress disorder of the traffic accident patient are associated with higher post-traumatic growth. Therefore, it is necessary to explore approaches that improve the social networks and resilience to help post-traumatic growth of traffic accident patients. Additional research is required through repetitive and long-term observation of the accident victims.

• ETRI Journal
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• v.32 no.1
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• pp.22-32
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• 2010

The pervasive game environments have activated explosive growth of the Internet over recent decades. Thus, understanding Internet traffic characteristics and precise classification have become important issues in network management, resource provisioning, and game application development. Naturally, much attention has been given to analyzing and modeling game traffic. Little research, however, has been undertaken on the classification of game traffic. In this paper, we perform an interpretive traffic analysis of popular game applications at the transport layer and propose a new classification method based on a simple decision tree, called an alternative decision tree (ADT), which utilizes the statistical traffic characteristics of game applications. Experimental results show that ADT precisely classifies game traffic from other application traffic types with limited traffic features and a small number of packets, while maintaining low complexity by utilizing a simple decision tree.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.