• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.53-59
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• 2021

According to the Korean Traffic Accident Analysis System (TAAS), more than 200,000 traffic accidents occur every year. Also, the statistics including auto insurance companies data show 1.3 million traffic accidents. In the case of TAAS, the types of traffic accidents are simply divided into four; frontal collision, side collision, rear collision, and rollover. However, more detailed information is needed to assess for advanced driver assist systems at intersections. For example, directional information is needed, such as whether the vehicle in the car accident way in a straight or a left turn, etc. This study intends to redefine the type of accident with the more clear driving direction and path by referring to the Negligence standards used in automobile insurance accidents. The standards largely divide five categories of car-to-car/motorcycle /pedestrian/cyclist, and highway, and the each category is classified into dozens of types by status of the traffic signal, conflict situations. In order to present more various accident types for auto insurance accidents, the standards are reclassified driving direction and path of vehicles from crash situations. In results, the car-to-car accidents are classified into 33 accident types, car-to-pedestrian accidents have 19 accident types, car-to-motorcycle accidents have 38 accident types, and car-to-cyclist accidents are derived into 26 types.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.47-51
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• 2010

A vehicle classification data is essential for traffic road planning and pavement. In this study, the vehicle height, vehicle criteria for classification applied to measure the height of the car driving has devised a way to install equipment. It is capable of measuring the vehicle height was confirmed to field experiments, the measurement system is obtained to the vehicle length and height data. In this experiment, results showed the accuracy of 88.6% compared to classification data using the discriminant function obtained from video replaying. The height of vehicle applying the classification criteria can be utilized to determine the vehicle class.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.39-50
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• 2006

This study aims to introduce a basic principle to improve the incident detection algorithm using traffic flow diagrams that can classify traffic states with a high reliability on the basis of the analysis of traffic flow characteristics under the recurrent or incident congestions. It is tried to newly classify the traffic states with the speed-flow and speed-occupancy diagrams. This is because McMaster algorithm has a tendancy on not identifying the traffic states exactly using the flow-occupancy diagram. In this study it shows that the classification of traffic states is applicable to use speed-occupancy relationship Therefore, it is necessary to determine some parameters to correctly classify the areas representing the traffic states and it may be possible to develop a new algorithm to detect the incident with a high reliability.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.128-134
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• 2006

Traffic information protocol in DMB is very different from existing analog broadcasting and wireless communication network. In this paper, we examined whether traffic information protocol of Europe Broadcasting Union(TPEG) is applicable to domestic DMB. Also, we proposed a division of classification on kinds of franc information, and related data that it is required to transmit traffic information of TPEG form. We composed of experiment equipment and studied whether is expressed traffic informations as like accident, event, traffic condition and CCTV image on car navigation system. The results obtained it can be given expression to phrases from TPEG streaming data and to link with electronics map by decoding TPEG straming data. Also it can be expressed CCTV and graphic image which is composed of TPEG form.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.14-20
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• 2024

Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.879-890
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• 2002

In this paper we consider open queueing system with a Poisson arrival process which have a finite upper bound on the arrival rate for which the system is stable. Interpolation approximations for quantities of interest, such as moments of the sojourn time distribution, have previously been developed for such systems, utilizing exact light and heavy traffic limits. These limits cannot always be easily computed for complex systems. Thus we consider an interpolation approximation where all of the relevant information is estimated via simulation. We show that all the relevant information can in fact be simultaneously estimated in a single regenerative simulation at any arrival rate. In addition to light and heavy traffic limits, both the quantity of interest and its derivative (with respect to the arrival rate) are estimated at the arrival rate of the simulation. All of the estimates are then combined, using a least squares procedure, to provide an interpolation approximation.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.7-29
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• 1996

Traffic flow characteristics is analysed on eight multi-lane roads which are unsignalized in urban areas. Data of traffic flow rates by classification and average speed were gathered every ten minutes interval for twenty-four hours. Machine (NC-90A) was used to acquire the field data. The major purpose of this study is to build up speed-density models on urban arterial roads. Five different kinds of models were tested. Those models are Greenshields' model, Greenberg's model, modified Greenberg's model, Underwood's model and Drake's model. The modified Greenberg's model fits best at six points and the Greenshield's model fits best two points out of eight points. The breakpoint(Kb) of modified Greenberg's model is between 10 and 32 pcphpl. Capacity drawn from speed-volume relationships were appeared to be arround 2,000 and 2,200 pcphpl at the Hannam Bridge and the Hannam Overpass and 1,100 and 1,700 pcphpl at Namsan Tunnel(No1) and the beginning point of Gyeong-Bu Expressway.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.587-590
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• 2004

In this paper proposed a HMM(Hidden Martov Model)-based segmentation method which is able to model shadows as well as foreground and background regions. Shadow of moving objects often obstruct visual tracking. We propose an HMM-based segmentation method which classifies in real time oath objects. In the case of traffic monitoring movies, the effectiveness of the proposed method has been proven through experimental results

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2160-2168
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• 2015

The SSL/TLS, one of the most popular encryption protocol, was developed as a solution of various network security problem while the network traffic has become complex and diverse. But the SSL/TLS traffic has been identified as its protocol name, not its used services, which is required for the effective network traffic management. This paper proposes a new method to generate service signatures automatically from SSL/TLS payload data and to classify network traffic in accordance with their application services. We utilize the certificate publication information field in the certificate exchanging record of SSL/TLS traffic for the service signatures, which occurs when SSL/TLS performs Handshaking before encrypt transmission. We proved the performance and feasibility of the proposed method by experimental result that classify about 95% SSL/TLS traffic with 95% accuracy for every SSL/TLS services.