• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.125-144
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• 2022

When a variable message signs (VMS) system displays false information related to traffic safety caused by malicious attacks, it could pose a serious risk to drivers. If the normal message patterns displayed on the VMS system are learned, it would be possible to detect and respond to the anomalous messages quickly. This paper proposes a method for detecting anomalous messages by learning the normal patterns of messages using a bi-directional generative pre-trained transformer (GPT) network. In particular, the proposed method was trained using the normal messages and their system parameters to minimize the corresponding negative log-likelihood (NLL) values. After adequate training, the proposed method could detect an anomalous message when its NLL value was larger than a pre-specified threshold value. The experiment results showed that the proposed method could detect malicious messages and cases when the system error occurs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.111-120
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• 2009

Restructuring urban land use patterns from the motor-oriented to transit-oriented ones is a contemporary trend in urban planning and transportation. It is expected that transforming urban land use patterns to be transit and walking friendly would resolve various urban problems such as heavy energy consumptions, air pollutions, and traffic congestions. Korean cities have much potentials for developing transit-oriented urban environments in terms of its density and civil service levels, but the level of transit usage levels and the environmental quality of cities are not good enough for supporting such transition. The purpose of this study is to analyze the urban transit center areas and identify the problems to be solved to create transit-oriented urban environments. Case studies of four urban transit center areas in Seoul Metropolitan region were conducted to examine the spatial characteristics of urban transit center areas and identify their problems. Development density, land use diversity, walk-ability and transit connectivity were the primary feature of interests.

• Clinical Pain
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• v.18 no.2
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• pp.59-64
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• 2019

Objective: This study evaluated the feasibility of ultrasound-guided lumbar nerve root block (LNRB) and S1 nerve root block by identifying spread patterns via fluoroscopy in cadavers. Method: A total of 48 ultrasound-guided injections were performed in 4 fresh cadavers from L1 to S1 roots. The target point of LNRB was the midpoint between the lower border of the transverse process and the facet joint at each level. The target point of S1 nerve root block was the S1 foramen, which can be visualized between the median sacral crest and the posterior superior iliac spine, below the L5-S1 facet joint. The injection was performed via an in-plane approach under real-time axial view ultrasound guidance. Fluoroscopic validation was performed after the injection of 2 cc of contrast agent. Results: The needle placements were correct in all injections. Fluoroscopy confirmed an intra-foraminal contrast spreading pattern following 41 of the 48 injections (85.4%). The other 7 injections (14.6%) yielded typical neurograms, but also resulted in extra-foraminal patterns that occurred evenly in each nerve root, including S1. Conclusion: Ultrasound-guided injection may be an option for the delivery of injectate into the S1 nerve root, as well as lumbar nerve root area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.821-830
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• 2017

The internet is an important infra resource that it controls the economy and society of our country. Also, it is providing convenience and efficiency of the everyday life. But, a case of various are occurred through an using vulnerability of an internet infra resource. Recently various attacks of unknown to the user are an increasing trend. Also, currently system of security control is focussing on patterns for detecting attacks. However, internet threats are consistently increasing by intelligent and advanced various attacks. In recent, the darknet is received attention to research for detecting unknown attacks. Since the darknet means a set of unused IP addresses, no real systems connected to the darknet. In this paper, we proposed an algorithm for finding black IPs through collected the darknet traffic based on a statistics data of port information. The proposed method prepared 8,192 darknet space and collected the darknet traffic during 3 months. It collected total 827,254,121 during 3 months of 2016. Applied results of the proposed algorithm, black IPs are June 19, July 21, and August 17. In this paper, results by analysis identify to detect frequency of black IPs and find new black IPs of caused potential cyber threats.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.136-144
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• 2015

It has reported that a median bus-stop island on an exclusive median bus corridor has shortened a unit crossing distance, encouraged a pedestrian's illegal jay walking, and thus increased the number of accidents in the area. Therefore, this study plans to analyze the various crossing patterns of pedestrians at exclusive median bus corridors. This study analyzes 30,184 pedestrian crossing data which are collected from the median bus-stops, 'Express Bus Terminal,' and reveals that the rate of spatial jaywalking was 37.8%. This rate is 11.1 times higher than the rate of traffic signal violation. Therefore, this study suggests that more research needs to be done to provide a traffic safety facilities for protecting spatial crossing pedestrians and preventing jaywalking and traffic signal violation.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.31-40
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• 2009

Some road charges toll to finance the cost or to manage traffic congestion. With a growth of PPI projects, toll roads would be increase continuously. Tolls have a considerable influence on user's route choice, and sometimes can affect to the departure time and even to mode choice. For modelling toll roads, user's WTP or VOT has an important role and it is general that VOT is equivalent to the wages of workers. The current way of modelling technique yields various toll price elasticity from low to high. When there exist few alternative routes, unrealistic result that all traffic assigned to some shortest path may occur. The toll price elasticity can be influenced by alternative route and congestion level, but some result shows nearly unrealistic patterns. The model to forecast more realistic toll road demand is very essential for estimating toll revenue, choice of optimal toll level & collecting location and establishing toll charge strategy. This paper reviewed some literatures about toll road modelling and tested case study about the assignment technique with different VOT. The case study shows that using different VOT yields more realistic result than the use of single VOT.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.233-239
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• 2006

Most research for until at now link travel time were research for mean link travel time calculate or estimate which uses the average of the individual vehicle. however, the link travel time distribution is divided caused by with the impact factor which is various traffic condition, signal operation condition and the road conditional etc. preceding study result for link travel time distribution characteristic showed that the patterns of going through traffic were divided up to 2 in the link travel times. therefore, it will be more accurate to divide up the link travel time into the one involving delay and the other without delay, rather than using the average link travel time in terms of assessing the traffic situation. this study is it analyzed transit hour distribution characteristic and a cause using examine to the variables which give an effect at link travel time distribute using simulation program and determinate link travel time distribute ratio estimation model. to assess the distribution of the link travel times, this research develops the regression model and the fuzzy model. the variables that have high level of correlations in both estimation models are the rest time of green ball and the delay vehicles. these variables were used to construct the methods in the estimation models. The comparison of the two estimation models-fuzzy and regression model- showed that fuzzy model out-competed the regression model in terms of reliability and applicability.