• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.2
• /
• pp.153-160
• /
• 2017

It may be reasonable to consider density per unit area over time rather than analyze traffic volume, which is simply the traffic volume per unit of time, in assessing the maritime traffic congestion of a certain area. This study contributes to the standardization of maritime traffic congestion assessment methods for the maritime traffic safety diagnosis institute while seeking a new method to minimize evaluation error due to converted traffic volume per ship tonnage level. To solve this problem, a method to evaluate maritime traffic congestion by comparing the area occupied by a vessel with the area of its route using vessel identification data from the Automatic Identification System (AIS) has been proposed. In this new model, it is possible to use actual data due to the development of information and communication technology, reducing conversion error while allowing for the evaluation of maritime traffic congestion by route.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.13-25
• /
• 2024

In traffic management, gaps in understanding traffic conditions continue to exist. While the self-belonging problem indicator develops relative to speed, belonging, and self-based relative inclination, it does not apply elimination criteria that may indicate situations that contrast with attribute-specific problems. In this study, we develop integrated indicators that specify communication situations and safety levels for modeling. We review indicators of changes in traffic conditions and raise safety issues, reviewing the indicators so that ITS data can be applied, analyzing the relationships between indicators through factor analysis. We develop combined, integrated indicators that can show changes and stability in traffic situations and that can be applied in traffic information centers to contribute to the development of a traffic environment that can monitor related traffic conditions.

• Safety and Health at Work
• /
• v.15 no.2
• /
• pp.208-212
• /
• 2024

Background: Traffic accidents involving professional drivers have serious societal repercussions. Unique occupational stressors and health risks exacerbate the likelihood of traffic accidents among professional drivers. This study explores the association between presenteeism-impaired work performance due to working while unwell-and traffic accident risk among professional taxi drivers in Japan. Methods: A prospective cohort study was conducted from June 2022 to February 2023, involving taxi drivers from a single company in Fukuoka Prefecture, Japan. Presenteeism was assessed using the Work Functioning Impairment Scale (WFun). Primary outcome involved the number of self-reported minor traffic accidents. The incidence rate ratio (IRR) of minor traffic accident occurrences was estimated using a Poisson regression analysis, adjusted for confounders including sex, age, and driving experience. Results: Of 838 targeted drivers, 435 were included in the analysis. Higher baseline work functioning impairment was associated with a significant trend of increasing IRR of minor traffic accidents (p for trend = 0.045). A dose-response relationship was seen between the degree of presenteeism and incidence rate of minor traffic accidents. Conclusion: Higher levels of presenteeism were associated with an increased risk of traffic accidents among taxi drivers. The findings underscore the need for socio-economic support and prioritized health management to mitigate traffic accident risk among professional drivers. This study highlights the importance of managing non-critical health issues alongside serious health conditions for safer driving practices among professional drivers in Japan.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.805-808
• /
• 2008

In this paper, it was performed the analysis on various intersection acoustic patterns for detection rate improvement of accident sound detection system : an acoustic pattern analysis on general traffic noise, an acoustic pattern analysis on engine noise, an acoustic pattern analysis on obstruct factors for accident sound detection system. There are remarkable differences between the acoustic patterns of traffic noise and accident sound, and we most consider the acoustic patterns when we compose the accident traffic detection system by acoustic because there is error range of 20[dB] according to the volume of traffic in intersection.

• Journal of Communications and Networks
• /
• v.18 no.5
• /
• pp.818-825
• /
• 2016

Network management and anomaly detection are challenges in high-speed networks due to the high volume of packets that has to be analysed. Flow-based analysis is a scalable method which reduces the high volume of network traffic by dividing it into flows. As sampling methods are extensively used in flow generators such as NetFlow, the impact of sampling on the performance of flow-based analysis needs to be investigated. Monitoring using sampled traffic is a well-studied research area, however, the impact of sampling on flow-based anomaly detection is a poorly researched area. This paper investigates flow sampling methods and shows that these methods have negative impact on flow-based anomaly detection. Therefore, we propose an efficient probabilistic flow sampling method that can preserve flow traffic distribution. The proposed sampling method takes into account two flow features: Destination IP address and octet. The destination IP addresses are sampled based on the number of received bytes. Our method provides efficient sampled traffic which has the required traffic features for both flow-based anomaly detection and monitoring. The proposed sampling method is evaluated using a number of generated flow-based datasets. The results show improvement in preserved malicious flows.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.22 no.4
• /
• pp.42-55
• /
• 2014

The purpose of this research was to identify expertise in ait traffic control by using cognitive skill analysis for novices and experts in routine and non-routine situations. The result of study was to understand expertise in air traffic control tasks in terms of what cognitive processes are responsible for the expert's high performance levels. The problem solving task was difficult for novices, but performed relatively automatically by experts in a routine situation. The difficulty could indicate the presence of controlled processing. Rather than rules and strategies, novices focused more on environmental factors, which merely increase cognitive load. In a non-routine situation, novices showed that they did not categorize the information consistently and alternative resources were not available for them. Experts, however, performed automatically a task by arranging and organizing information related to problem solving components in contexts without regard to a routine and non-routine situation. Especially experts developed a stable representation and directed alternative resources for air traffic flow and efficiency. Based on the results, cognitive processes of experts could be useful to understand expert performance and analyze the learning process, which imply the necessity of developing expertise systematically.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.3
• /
• pp.125-132
• /
• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.20 no.2
• /
• pp.172-178
• /
• 2010

The multiple regression analysis and path analysis in each dominant area of noise source are conducted to analyze the relationship between dependent variables like annoyance and independent ones such as noise and non-noise factors. The multiple regression analysis shows that impact of noise factors is the highest to annoyance in dominant areas of road traffic and railway noise. Meanwhile, impact of non-noise factors such as sensitivity and satisfaction of environment on annoyance is also high in these areas. The path analysis result for multivariate analysis between various independent and dependent variables is similar to that of the multiple regression analysis. However, noise factor is the greatest factor influent on annoyance in the dominant areas of the combined noise, and relationship between annoyance and sensitivity is the highest in combined area exposed to road traffic noise and railway noise.

• Journal of the Korean Geosynthetics Society
• /
• v.1 no.1
• /
• pp.53-61
• /
• 2002

Even though a lot of laboratory and field tests for asphalt pavements using geosynthetics have been conducted recently, any rational and systematic analysis for the economic efficiency of the asphalt pavement systems reinforced by using geosynthetics has not been proposed yet. In this study, the economic analysis considering the traffic characteristics for the glass fiber sheet reinforced asphalt pavement was performed using the Life Cycle Cost Analysis(LCCA) which is commonly used for the economic analysis technique. The economic efficiency for the glass fiber sheet reinforcement and the traffic characteristics was examined by applying the test results from the literature review to the economic analysis model.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.9
• /
• pp.2129-2137
• /
• 2015

Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.