• Journal of Korean Institute of Industrial Engineers
• /
• v.37 no.4
• /
• pp.297-303
• /
• 2011

Major ports in Northeastern Asia engage in fierce competition to attract transshipment traffic volume. Existing time series analyses for analyzing port competition relationships examine the types of competition and relations through the signs of coefficients in cointegration equations using the transshipment traffic volume results. However, there are cases for which analyzing competing relationships is not possible based on the results of the transshipment traffic volume data differences and limitations in the forecasting of traffic volume. Accordingly, we used the Lotka-Volterra (L-V) model,also known as the ecosystem competitive relation model, to analyze port competition relations for the long-term forecast of South Korean transshipment traffic volume.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.88-97
• /
• 2023

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.7C
• /
• pp.670-676
• /
• 2002

The object of ATM network is to the guarantee quality of service(QoS). Therefore, various of traffic management schemes have been proposed. Among these schemes, call admission control(CAC) is very important to provide real-time services and ON-OFF model, which is single source traffic model, has been used. But ON-OFF model differ from GCRA(Generic Cell Rate Algorithm) controlled traffic in ATM network. In this paper, we analyze the traffic, which is controlled as dual GCRA, and propose TWM(Three-state Worst-case Model), which is new single source traffic model. We also proposed CAC to guarantee peak-to-peak CDV(Cell Delay Variation) based on the TWM. In experiments, ON-OFF model and TWM are compared to show that TWM is superior to ON-Off model in terms of QoS guaranteeing.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.4
• /
• pp.92-98
• /
• 2022

This paper proposes a new method of the traffic signal detection and the recognition in an HSI color model. The proposed method firstly converts a ROI image in the RGB model to in the HSI model to segment the color of a traffic signal. Secondly, the segmented colors are dilated by the morphological processing to connect the traffic signal light and the signal light case and finally, it extracts the traffic signal light and the case by the aspect ratio using the connected component analysis. The extracted components show the detection and the recognition of the traffic signal lights. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the detection and the recognition of traffic signals.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.2
• /
• pp.20-29
• /
• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.709-714
• /
• 2008

The various computer programs are used in computer simulation of the traffic noise prediction. But the difference or problem of calculation method used for road traffic noise prediction is not exactly investigated. In this paper, Road traffic noise is predicted on the specific regions by using four prediction methods such as XPS31-133 model(France), RLS-90 model(Germany), ASJ RTN model(Japan) and FHWA model(U.S.A.), which are operated by a program named SoundPLAN, a program to predict road traffic noise. Those prediction values are compared with a measurement value. The results show that four prediction values for taraffic noise are a little different, because of various input factors according to the prediction methods.

• Journal of the Korean Society of Safety
• /
• v.30 no.2
• /
• pp.70-76
• /
• 2015

This study deals with the traffic accident of circular intersections. The purpose of the study is to develop the accident models by traffic violation type. In pursuing the above, this study gives particular attention to analyzing various factors that influence traffic accident and developing such the optimal models as Poisson and Negative binomial regression models. The main results are the followings. First, 4 negative binomial models which were statistically significant were developed. This was because the over-dispersion coefficients had a value greater than 1.96. Second, the common variables in these models were not adopted. The specific variables by model were analyzed to be traffic volume, conflicting ratio, number of circulatory lane, width of circulatory lane, number of traffic island by access road, number of reduction facility, feature of central island and crosswalk.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.55-58
• /
• 2005

This study was Performed to grasp of the problem and improvement in traffic noise environmental impact assessment(EIA). National institute of environmental research(NIER) traffic noise prediction model is in general use in internal EIA. In this study, NIER noise prediction model need to improve in that the predicted results lower than the measured results. The other predict model(KLC KEI) is more accurate. Also the volume and speed of traffic is need to standardize in traffic noise prediction.

• Journal of the Korean Institute of Plant Engineering
• /
• v.23 no.4
• /
• pp.21-27
• /
• 2018

This paper proposed the real-time monitoring methodology of a traffic signal controller. The proposed methodology is based on the simulation technology, and it is necessary to construct a simulation model imitating the behavior of a traffic signal controller. By executing the simulation model, we can obtain the 'nominal system trajectory' of the traffic signal controller. On the other hand, an IoT(Internet of Things)-based monitoring device is implemented in a traffic signal controller. Through the monitoring device, it is possible to obtain the 'actual system trajectory'. By comparing the nominal system trajectory and the actual system trajectory, we can estimate the degree of deterioration of a traffic signal controller.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2002.05a
• /
• pp.163-168
• /
• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment was designed in within-subject design with two factors; vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods; the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.