• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.582-585
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• 2018

As traffic volume increases and road networks become more complicated, identifying for accurate traffic flow and driving smooth traffic flow are a concern of many countries. There are various analytical techniques and studies which desire to study about effective traffic flow. However, the necessary activity is finding the traffic flow pattern through data visualization including location information. In this paper aim to study a real-world urban traffic trajectory and visualize a pattern of traffic flow with a simulation tool. Our experiment is installing the sensor module in 40 taxis and our dataset is generated along 24 hours and unscheduled routes. After pre-processing data, we improved an open source traffic visualize tools to suitable for our experiment. Then we simulate our vehicle trajectory data with a dots animation over a period of time, which allows clearly view a traffic flow simulation and a understand the direction of movement of the vehicle or route pattern. In addition we further propose some novel timelines to show spatial-temporal features to improve an urban environment due to the traffic flow.

• Journal of Korean Society of Transportation
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• v.6 no.2
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• pp.35-48
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• 1988

Amongst various analytic methods of internal structure of city, the factor analysis method which uses O-D matrix data has some merits and characteristics compared to other methods. 1) It is possible to find one certain interaction and flow pattern between traffic zones with in a city through reanalyzing O-D data which is too complex to grasp specific meaning or pattern of flow systems. 2) It can be easily visualized the traffic flow pattern by using adequate graphic techniques, and also can clarify the functional areas whose interaction linkages are significantly strong enough between each other. In this study, the taxi traffic O-D data between 42 traffic zones in Kwang-ju city was reanalyzied by varimax rotated factor analysis methods. As a result, four factors that have significant level factor loading (over 0.5 ) and factor score (over 1.0) were sorted out. so to speak four different functional areas were clarified in Kwang-ju city, of the West, the East, the south, and the North functional areas whose interaction linkages are significantly strong enough between each other. In the study, the taxi traffic O-D data between 42 traffic zones in Kwang-ju city was reanalyzied by varimax rotated factor analysis methods. As a result, four factors that have significant level factor loading (over 0.5) and factor score 9over 1.0) were sorted out. so to speak four different functional areas were clarified in Kwang-ju city, of the West, the East, the South, and the North functional area, then these four functional areas are almost coincided with citizen's general conception of community division and administrative district. Accordingly the factor analysis methods using traffic data seems to proved to be very accurate and useful analytic instruments for analyzing flow pattern and clarifying functional areas of city, and believed to provide basic informations and criteria for practical urban land use planning and transportation planning.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.183-190
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• 2012

PURPOSES : The fundamental diagram provides basic information necessary in the analysis of traffic flow and highway operation. When traffic flow is congested, the density-flow points in the fundamental diagram are widely scattered and move in a stochastic manner. This paper investigates the pattern of density-flow point transitions and identifies car-following behaviors underlying the density-flow transitions. METHODS : From a microscopic analysis of 722 fundamental diagrams of NGSIM data, a total of 20 transition patterns of fundamental diagrams are identified. Prominent features of the transition patterns are explained by the behavior of the leader and follower. RESULTS : It is found out that the average speed and the speed difference between the leader and the follower critically determine the density-flow transition pattern. The density-flow path is very sensitive to the values of vehicle speed and spacing especially at low speed and high density such that most fluctuations in the fundamental diagram in the congested regime is due to the noise of speed and spacing variations. CONCLUSIONS : The result of this study suggests that the average speed, the speed difference between the leader and the follower, and the random variations of speed and spacing are dominant factors that explain the transition patterns of a fundamental diagram.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.159-166
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• 2004

The capacity concept presented in the Highway Capacity Manual is for steady-state traffic flow assuming that there is no restriction in downstream flowing, which is traditionally used for planning, design, and operational analyses. In the congested traffic condition, the control objective should be to keep the congested regime from growing and to recover the normal traffic condition as soon as possible. In this control case, it is important to predict the spatial-temporal pattern of congestion evolution or dissipation and to estimate the throughput reduction according to the spatial-temporal pattern. In this context, the new concept of dynamic capacity for managing congested traffic is developed in terms of spatial-temporal evolution of downstream traffic congestion and in view of the 'input' concept assuming that flow is restricted by downstream condition rather than the 'output' concept assuming that there is no restriction in downstream flowing (e.g. the mean queue discharge flow rate). This new capacity is defined as the Maximum Sustainable Throughput that is determined based on the spatial-temporal evolution pattern of downstream congestion. And the spatial-temporal evolution pattern is estimated using the Newell's simplified q-k model.

• 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.32 no.5
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• pp.522-530
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• 2014

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s. Such dataset provides a pool of spatio-temporally experienced traffic conditions. Traffic flow pattern is known as spatio-temporally recurred, and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. These imply that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. TMCs are reluctant to employ the black-box approaches even though there are numerous valuable articles. This research proposes a more readily understandable and intuitively appealing data-driven approach and developes an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

• 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.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.7-14
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• 2011

This paper discusses operational technique and effectiveness of Variable Speed Limits system that is implemented to control the traffic-flow on the Naebu Expressway. As the first step of the analysis, traffic data collected from vehicle detectors are corrected and smoothed. Applying a pattern analysis technique to the traffic data, the weekday traffic is classified into four different groups, and median of each group is calculated. Using three state variables, i.e., diverted traffic volume, average density and average speed, the conditions of roadway segments are determined. Computational outputs resulted from the application of the proposed model to the scenarios show that implementation of Variable Speed Limits system improved both safety and efficiency of the expressway. For the operational strategy, this paper also presents the change rate of the speed limit, and the effective duration of the speed limit according to the entering traffic volume.

• Proceedings of KOSOMES biannual meeting
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• 2018.06a
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• pp.49-49
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• 2018

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.833-839
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• 2019

Bus Information System (BIS) collects information related to the operation of buses and provides information to users through predictive algorithms. Method of predicting through recent information in same section reflects the traffic situation of the section, but cannot reflect the characteristics of the target line. The method of predicting the historical data at the same time zone is limited in forecasting peak time with high volatility of traffic flow. Therefore, we developed a pattern recognition bus arrival time prediction algorithm which could be overcome previous limitation. This method recognize the traffic pattern of target flow and select the most similar past traffic pattern. The results of this study were compared with the BIS arrival forecast information history of Seoul. RMSE of travel time between estimated and observed was approximately 35 seconds (40 seconds in BIS) at the off-peak time and 40 seconds (60 seconds in BIS) at the peak time. This means that there is data that can represent the current traffic situation in other time zones except for the same past time zone.