• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.506-517
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• 2012

Electronic Toll Collection System (ETCS), so called "Hi-Pass" in Korea, has improved traffic flow at toll gate of highways. It is known that the improvement of traffic flow should reduce air pollutants and $CO_2$ from vehicles. In this study, real driving emission of a light duty truck with Portable Emission Measurement System(PEMS) has been measured to evaluate the emission reduction effect due to ETCS. The correlations between driving variables and emissions have been analyzed to verify its effect on traffic flow improvement and emission reduction at toll gate. We considered average vehicle speed, Relative Positive Acceleration (RPA), and the distance of queue as driving variables. Compared to passing Manual Toll Collection System (MTCS) lane without queue, ETCS was able to reduce 38.7% of $NO_x$, 21.6% of soot, and 27.7% of $CO_2$. The results showed that the higher the average vehicle speed, the lower RPA and no queue in ETCS contributed to the emission reductions. Linear equation models with RPA and queue have been established by the multiple linear regression method. The linear models resulted in the higher coefficient of determination than those with only average vehicle speed used for establishing vehicle emission factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.6
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• pp.350-359
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• 2014

With the rapid growth of Internet, the importance of application traffic analysis is increasing for efficient network management. The statistical information in traffic flows can be efficiently utilized for application traffic identification. However, the packet out-of-order and retransmission occurred at the traffic collection point reduces the performance of the statistics-based traffic analysis. In this paper, we propose a novel method to detect and resolve the packet out-of-order and retransmission problem in order to improve completeness and accuracy of the traffic identification. To prove the feasibility of the proposed method, we applied our method to a real traffic analysis system using statistical flow information, and compared the performance of the system with the selected 9 popular applications. The experiment showed maximum 4% of completeness growth in traffic bytes, which shows that the proposed method contributes to the analysis of heavy flow.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.7-17
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• 1999

The expressway network includes a total of about 1,899 km in our country The only 1,016 km of that is being managed by the closed Toll Collection System(TCS) which is composed of 74 tollgates. We obtain inter-tollgate O-D matrices from that system everyday. But, they are not traffic-zonal O-D matrices. So they have not been used for the expressway traffic analysis and the traffic demand estimation despite of their accuracy. If we could estimate the traffic-zonal O-D matrices from TCS O-D ones, we could perform expressway traffic analysis more efficiently. Moreover we could obtain more precise expressway O-D matrices and traffic-zonal O/D ones by this approach than by the conventional ones. In this paper. we proposed the model estimating traffic-zonal O/D matrices from TCS O-D ones. The assigned volumes with the estimated traffic-zonal O-D matrices produced the only 17.9% error all over the TCS expressway section when compared to the real traffic volumes. So, the proposed model enables for us to estimate more accurate O/D matrics than any other existing methods.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.119-128
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• 2021

The most typical method to generate traffic information is installing vehicle detectors and collecting various traffic variables. The information collection accuracy of a vehicle detector affects the reliability of the generated traffic information. The most universal vehicle detector is an image detector. This study installed a magnetic detector in the same position as an image detector and evaluated the accuracy of traffic volume and speed data depending on a variety of environment. Based on the evaluation, more errors occurred as the image detector was placed farther from the camera, whereas more errors were found to occur during the night rather than the day. Although rainfall did not affect the collection of traffic volume, it negatively affected speed data collection. Therefore, an analysis of the camera's view angle and its optimization depending on the camera installation position and height are required to enhance the currently operated image detector performance. It is judged that a separate performance evaluation criterion should be prepared in a bad weather environment.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.773-776
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• 2000

Traffic monitoring plays an important role in intelligent transportation systems. It can be used to collect real-time traffic data concerning traffic flow. Passive shadows resulted from roadside buildings or trees and active shadows caused by moving vehicles, are one of the factors that arise errors in vision based vehicle detection. In this paper, a land mark based method is proposed for vehicle detection and shadow rejection, and finally vehicle count are achieved based on the land mark detection method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.

• 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.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.214-218
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• 2007

• Korean Journal of Occupational Health Nursing
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• v.20 no.2
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• pp.172-184
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• 2011

Purpose: The aim of this study is to identify the characteristics of occupational accidents by work type among municipal sanitation workers. Methods: The original data of occupational accidents in 2009 by Korea Occupational Safety & Health Agency were analyzed by frequency and percentage using the SAS Version 9.1. Results: The types of occupational accident were as follows: slips and trips, falls, musculoskeletal disorders, traffic accident, collision, amputation, cut & puncture, crush injuries, strenuous movement and drop/fly. Slips and trips occurred most frequently in domestic waste collection and street sweeping. The traffic accident showed the highest incidence in food waste collection. Falls occurred most frequently in recycling waste collection. Musculoskeletal disorders showed the highest incidence in large waste collection. Conclusion: Depending on the work type, types of occupational accident were different. The results of this study can provide the information for the development of the occupational accident prevention programs of municipal sanitation workers.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.273-279
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• 2022

Owing to the need to establish a cooperative-intelligent transport system (C-ITS) environment in the transportation sector locally and abroad, various research and development efforts such as high-tech road infrastructure, connection technology between road components, and traffic information systems are currently underway. However, the current central control center-oriented information collection and provision service structure and the insufficient road infrastructure limit the realization of the C-ITS, which requires a diversity of traffic information, real-time data, advanced traffic safety management, and transportation convenience services. In this study, a network construction method based on the existing received signal strength indicator (RSSI) selected as a comparison target, and the experimental target and the proposed intelligent edge network compared and analyzed. The result of the analysis showed that the data transmission rate in the intelligent edge network was 97.48%, the data transmission time was 215 ms, and the recovery time of network failure was 49,983 ms.