• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.99-108
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• 2010

The traffic classification is a preliminary but essentialstep for stable network service provision and efficient network resource management. While various classification methods have been introduced in literature, the payload signature-based classification is accepted to give the highest performance in terms of accuracy, completeness, and practicality. However, the collection and maintenance of up-to-date signatures is very difficult and time consuming process to cope with the dynamics of Internet traffic over time. In this paper, We propose an automatic payload signature generation mechanism which reduces the time for signature generation and increases the granularity of signatures. Furthermore, We describe a signature update system to keep the latest signatures over time. By experiments with our campus network traffic we proved the feasibility of our mechanism.

• Journal of KIISE:Information Networking
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• v.41 no.4
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• pp.177-185
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• 2014

It has been occurred many times that wireless sensor networks (WSNs) had congested areas because all the sensing data collected by multiple sensor nodes are delivered to one sink node. Typically, in order to control congested areas, it used to reduce the traffic by increasing the sensing period or discarding packets. However, those schemes have a disadvantage that it loses the reliability when delivering sensing data. Moreover, there are no schemes considering case of having poor quality of links between nodes in practical terms. In this paper, we suggest a scheme not to reduce the traffic but to distribute the traffic by changing routing paths depends on the traffic and the quality of links. Also, it can be seen that the reliability of delivering of the sensing data is improved with the experiments improving collection rates and shortening end-to-end delay.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.21-30
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• 2002

Busan Port which is the representative social overhead capital facilities for international trade of goods has been the driving force for economic development in Korea. Therefore, the central government should play the major role in building a rear road to Busan Port in order that it may function as a mojor port of Northeastern Asia through the systemization of mutual assistance among connected facilities, completely equipped with port-related facilities befitting to the principal port of imports and exports. In this study, the validity of container tax is being examined, analyzing container tax which Is considered as an obstacle to the development of Busan Port and its purpose, and grasping the present conditions by the realistic speculation on container tax issues and its abolition. First, the port rear road as a social overhead capital facilities, which connects port and expressway, should be considered as part of port, and port is social overhead capital invested by government. Second, the susan City imposes on container tax. As a result, a shipper and a shipping company are paying a double charge by paying container tax with port dues. Third, someone argues that port rear road is constructed as container truck generates much traffic, but it is unreasonable to conclude that container truck is seed of traffic jam because according to traffic survey of Busan City, container truck occupies only 1.45% of total traffic Forth, it has bad influence upon the competitiveness of Busan Port as Northeastern logistics base, as other ports who are competing with Busan Port like Singapore. Hong kong, Kobe make their competitiveness strong by decreasing the cost of pore dues

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.20-29
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• 2014

Density is applied both three major macroscopic traffic variables (traffic volume, speed, and density) and two measures of effectiveness (MOE) for level of service (LOS) on highway (density and V/C). Especially, it is known for the most accurate MOE on evaluating the LOS of highway. Despite such importance, there is a lack of study on density relatively than other variables for its difficulty of measurement. Existing density estimation methods have some limitations such as density values of same traffic flow vary with collecting time. In this study, we researched actual density measuring method with panoramic image, after each CCTV images in the Sapaesan Tunnel on Seoul Ring Expressway are matched into one panoramic image. Analysis through the Central Limit Theorem shows that density of 24 1 km-images, which means 24 second, applies traffic situation well. That is to say that reasonable density value regardless of collecting time, and practical density which represents actual traffic flow can be taken in case of measuring density by suggested collecting cycle.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.47-63
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• 2011

A traffic detection system can be deemed as a traffic data and information collection system to serve traffic policies, traffic management, and user services. The system plays a crucial role in verifying whether or not the current traffic system has issues or problems by checking out traffic data. In addition, the system does so in finding out a point or a section where an issue or a problem has occurred, if any, and in examining the causes of the issue or problem, the extent of its impact that has occurred and spread, and a method for resolving it. However, the existing point detection system of Korea has too many flaws. In order to fix the flaws, in this paper, the theoretical characteristics of the section detection system were researched in relation to the calculation of travel time. In addition, the travel time of probe cars was obtained by field survey, and it was compared to that of spot and section detection data. Then, simulation was performed to determine the optimal section detection interval. In conclusion, introduction of optimal section detection system was examined in order to achieve the advanced road management including traffic policy, traffic management, and user services.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.845-853
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• 2017

The main reason for urban traffic problems is the increasing in the use of private cars. In order to solve this problem, strong traffic demand management policies such as the collection of congestion tolls and the crackdown on freight vehicles are emerging. Now, however, it is necessary to seek ways for users to make changes in traffic behavior through direct communication between public agencies and passenger car users. As a technique to make this possible, mobility management, which aims to use rational passenger cars by inducing behavior change through individual consciousness change, has recently been proved to be effective in overseas. Therefore, this study concluded that the introduction of MM, which emphasizes individual voluntary behavior change, is necessary at present. In order to investigate the ways of inducing voluntary changes in traffic behavior of passenger car users, a questionnaire survey was conducted on traffic experts and the priorities of voluntary traffic behavior change measures were calculated using AHP. As a result of the analysis, it was shown that "to actively appeal to change the traffic behavior by using the mass media" was the top priority. The next priority were in order of company education for the change of traffic behavior, education for the school, and small-scale workshops.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.97-111
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• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.15-31
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• 2024

The characteristics of elderly traffic accidents were identified by reflecting the situation of the elderly population in Korea, which is entering an ultra-aging society, and the relationship between independent and dependent variables was analyzed by classifying traffic accidents of serious or higher and traffic accidents of minor or lower in elderly pedestrian traffic accidents using binomial variables. Data collection, processing, and variable selection were performed by acquiring data from the elderly pedestrian traffic accident analysis system (TAAS) for the past 10 years (from 13 to 22 years), and basic statistics and analysis by accident factors were performed. A total of 15 influencing variables were derived by applying the logistic regression model, and the influencing variables that have the greatest influence on the probability of a traffic accident involving severe or higher elderly pedestrians were derived. After that, statistical tests were performed to analyze the suitability of the logistic model, and a method for predicting the probability of a traffic accident according to the construction of a prediction model was presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.129-140
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• 2004

In this paper, we conduct the research about optimal aggregation interval of travel time data on interrupted traffic flow and verify the reliability of AVI collected data by using car plate matching method in RTMS for systematic collection and analysis of link travel time data on interrupted traffic flow rural arterial. We perform Kolmosorov-Smirnov test on AVT collected sample data and on entire population data, and conclude that the sample data does not represent pure random sampling and hence includes sample collection error. We suggest that additional review is necessary to investigate the effectiveness of AVI collected sample data as link representative data. We also develop statistical model by applying two estimation techniques namely point estimation and interval estimation for calculating optimal aggregation interval. We have implemented our model and determine that point estimate is preferable over interval estimate for exactly selecting and deciding optimal aggregation interval. Our final conclusion is that 5-minute aggregation interval is optimal to estimate travel time in RTMS, as is currently being used our investigation is based on AVI data collected from Yang-ji to Yong-in $42^{nd}$ National road.