• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.170-181
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• 2009

When a slow-moving vehicle occupies one of the lanes of a multi-lane highway, it often causes queuing behind, unlike one is caused by an actual stoppage on that lane. This happens when the traffic flow rate upstream from the slow vehicle exceeds a certain critical value. This phenomena is called as the Moving Bottleneck, defined by Gazis and Herman (1992), Newell (1998) [3], and Munoz and Daganzo (2002), who conducted the flow estimates of upstream and downstream and considered slow-moving vehicle speed and the flow ratio exceeding slow vehicle and the microscopic traffic flow characteristics of moving bottleneck. But, a study of delay on moving bottleneck was not conducted until now. So this study provides a average delay time model related to upstream flow and the speed of slow vehicle. We have chosen the two-lane highway and homogeneous traffic flow. A slow-moving vehicle occupies one of the two lanes. Average delay time value is a result of AIMSUN[9], the microscopic traffic flow simulator. We developed a multiple regression model based on that value. Average delay time has a high value when the speed of slow vehicle is decreased and traffic flow is increased. Conclusively, the model is formulated by the negative exponential function.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.42-46
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• 2006

As the next generation internet (NGI) is supporting High-speed in Gbps rate with the appearance of advanced network technologies, various applications that require high data rates. have been experimented and operated. By using high speed application services, many kinds of problems can be generated but we cannot easily grasp their reasons. However, if the user monitors the end-to-end one's flow data information (path and data rate in each router, state of each router) he can find them more accurately Until now, we have found out the fact that systems which can network-widely monitor end-to-end flow have not be developed yet, only simple systems which can monitor user's individual flow data at just one node are developed In this study, we suggest and materialize a system which can analyze bandwidth in real time by searching routing paths and measuring packet transfer rate between end-to-end flow data and supported flow traffic of using IPv6.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.97-116
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• 1994

Today advanced traffic management systems are required because of a high increase in traffic demand. Accordingly, the objective of this study is to take advantage of image processing systems and present image processing methods available for collection of the data on traffic characteristics, and then to investigate the possibility of traffic flow analysis by means of comparison and analysis of measured traffic flow. Data were collected at two places of Daegu city and Kyongbu expressway by using VTR. Rear view (down stream) and frontal view (up stream) methods were employed to compare and analyze traffic characteristics including traffic volume, speed, time-headway, time-occupancy, and vehicle-length, by analysis of measured traffic flow and image processing respectively. Judging from the results obtained by this study, image processing techniques are sufficient for the analysis of traffic volume, but a frame grabber equipped with high speed processor is necessary as well, with low level system judged to be sufficient for traffic volume analysis.

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

• ETRI Journal
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• v.30 no.2
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• pp.205-215
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• 2008

Many control schemes have been proposed for flow-level traffic control. However, flow-level traffic control is implemented only in limited areas such as traffic monitoring and traffic control at edge nodes. No clear solution for end-to-end architecture has been proposed. Scalability and the lack of a business model are major problems for deploying end-to-end flow-level control architecture. This paper introduces an end-to-end transport architecture and a scalable control mechanism to support the various flow-level QoS requests from applications.

• Journal of Navigation and Port Research
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• v.31 no.1 s.117
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• pp.43-48
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• 2007

Maritime traffic engineering is a technical field that observes the flow of vessels' traffic in accurate and describes the feature of ship's movement statistically, then contributes for the improvement of traffic flow and the safety of traffic. The flow of marine traffic can be controlled by carrying out assessment and analysis of vessel's traffic. It can realize the safety of marine traffic by accurate research and analysis of vessel's traffic, understanding its flow and analysis data of vessel traffic. This study shows the analysis system of marine traffic connected with Radar, AIS based on ENC(Electronic Navigational Chart). The marine traffic analysis system contributes to the safety of marine traffic through the design of marine traffic route, harbour facilities and improvement of vessels' traffic flow.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.127-132
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• 2006

Maritime transportation engineering is a technical field that observes the flow of vessel's traffic in accurate and describes the feature of ship's movement statistically, then contributes to the improvement of traffic flow and the safety of traffic. The flow of marine traffic can be controlled by carrying out assessment and analysis of vessel's traffic. It can realize the safety of marine traffic by accurate research and analysis of vessel's traffic, understanding its flow and analysis data of vessel traffic. This study the analysis system of marine traffic connected with Radar, AIS based on ENC(Electronic Navigational Chart). The marine traffic analysis system contributes to safety of marine traffic through the design of marine traffic route, harbour facilities and improvement of vessel's traffic flow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.135-142
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• 2006

The flow-density relations represent equilibrium relations between flow and density in the stationary state. Using individual vehicle data this paper proposed a method to 131ter traffic data in the stationary state and showed flow-density relations produced by the traffic data in the stationary state. The Proposed method is based on the idea that free flow and congested flow show totally different traffic behaviors and time series of the traffic data observed at detection stations. The traffic data collected from the stationary state in the free flow using this filtering method consist in the left branch of the flow-density relation and the traffic data collected from the stationary state in the congested flow consist in the right branch of the flow-density relation. The traffic data in the stationary state skew reproducible flow-density relation in the almost whole range of the traffic flow.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1504-1507
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• 1997

In this paper, we investigate a traffic flow modeled by stochastic Petri nets. The model consists of two parts : the traffic flow model and signal controller model. These models are used for analyzing the flow of the traffic intersection. The results of the evaluation are derived from a Petri Net-based simulation package, Greatspn. Through simulation we compare the performances of the pretimed signal controller with those of the trafic-adaptive signal controller.