• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.45-50
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• 2022

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.117-127
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• 2009

This study analyzed the uncertainty of the forecasted link traffic flow, and estimated of the interval link flow using Korea Transport Data Base (KTDB) to consider those risks into the feasibility study. In the paper, the uncertainty was analyzed according to the stochastic variation of the KTDB origin-destination traffic. It was found that the uncertainty of the entire network traffic forecasts was 15.4% in average,. when the stochastic variation of the KTDB was considered. The results showed that the more congested the roads were, the bigger the uncertainty of forecasted link traffic flow were found. In particular, we estimated the variance of the forecasted traffic flow, and suggested interval estimates of the forecasted traffic flow instead of point estimates which were presented in the common feasibility studies. These results are expected to contribute the quantitative evaluation of uncertain road investment projects and to provide valuable information to the decision makers for the transport investment.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.119-126
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• 2014

PURPOSES: Used in transportation planning and traffic engineering, almost traffic simulation tools have input variable values optimized by overseas traffic flow attribution because they are almost developed in overseas country. Thus, model calibration appropriated for internal traffic flow attribution is needed to improve reliability of simulation method. METHODS : In this study, the traffic flow model calibration is based on expressways. For model calibration, it needs to define each expressway link according to attribution, thus it is classified by design speed, geometric conditions and number of lanes. And modified greenshield model is used as traffic flow model. RESULTS : The result of the traffic model calibration indicates that internal congested density is lower than overseas. And the result of analysis according to the link attribution indicates that the more design speed and number of lanes increase, the lower the minimum speed, the higher the congested density. CONCLUSIONS: In the traffic simulation tool developed in overseas, the traffic flow is different as design speed and number of lanes, but road segment don't affect traffic flow. Therefore, these results need to apply reasonably to internal traffic simulation method.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.95.2-95
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• 2002

1. Introduction of Camera Detection system Camera Detection system is an equipment that can detect realtime traffic information by image processing techniques. This information can be used to analyze and control road traffic flow. It is also used as a method to detect and control traffic flow for ITS(Intelligent Transportation System). Traffic information includes speed, head way, traffic flow, occupation time and length of queue. There are many detection systems for traffic data. But video detection system can detect multiple lanes with only one camera and collect various traffic information. So it is thought to be the most efficient method of all detection system. Though the...

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• 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.

• Journal of the Korea Society for Simulation
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• v.11 no.4
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• pp.43-55
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• 2002

Vessel traffic safety management means the managerial technical measures for improving the marine traffic safety in general terms. The main flow of vessel traffic safety management is that: 1) Traffic Survey, 2) Replay by Marine Traffic Flow Simulation, 3) Quantitative Assessment, 4) Policy Alternatives, 5) Prediction·Verification. In the management of vessel traffic safety, it is most important to establish assessment models that can numerically estimate the current safety level and quantitatively predict the correlation between the measures to be taken and the improvement of safety and the reduction of ship handling difficulties imposed on mariners. In this paper, the replay model for traffic flow simulation was made using marine traffic survey data, and the present traffic situation became replay in the computer. An attempt was made to rate the current safety of ports and waterways by applying the Environmental Stress model. And, as a countermeasure for traffic management, by taking of, the promotion of total traffic congestion in early morning rush hour, the correlation between traffic control rate and the reduction in ship handling difficulties imposed on mariners was predicted quantitatively.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.84-98
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• 1995

Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic. It is presumed that upstream traffic volume can be used to predict the downstream traffic in a specific time period. Three models were developed for traffic flow prediction; a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models were evaluated using regression analysis. The third model is found to provide the best prediction for the analyzed data. In order to balance the variables appropriately according to the present traffic condition, a heuristic adaptive weighting system is devised based on the relationships between the beginning period of prediction and the previous periods. The developed models were applied to 15-minute freeway data obtained by regular induction loop detectors. The prediction models were shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30-to 45-minute. It is also found that the combined models usually produce more consistent forecasts than the historical average.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.623-631
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• 2011

In this study, a new method which can detect incidents in interrupted traffic flow was suggested. The applied method of detecting the incident is the Latin Square Analysis Method by using traffic traits. In the Latin Square Analysis, unlike other previously tried methods, the traffic situation was analyzed, this time considering the changes in traffic traits for each lane and for each time period. The data used in this study were the data observed in the actual field with fine weather. The traffic volumes, the vehicle speed and the occupancy rate were collected on the interrupted flow road. The data were collected in normal and incident situations. The incidents occurred on the second lane, the time of persistent incidents was set to 10 minutes. The Latin Square Analyses were performed using the collected data with the traffic volume, with the vehicle speed or with the occupancy rate. As a result in this study, in case of detecting the traffic situations with Latin Square Analysis, it will be more successful to apply traffic volume to detect the traffic situations than to apply other factors.