• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.150-158
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• 2014

A bunch of asphalt roads have been damaged frequently in relation to the rapid climate change. To solve and prevent this type of problems, many nationalities in the world have performed various researches. In this regard, the objective of this study is to develop prediction model as to the number of potholes occurred in seoul. At the same time, we have utilized empirical and statistical approaches in order for us to identify factors which is affecting the actual occurrence. The predictive model was determinded by using BHS (Basic Harmony Search) algorithm. Prediction was based on the weather and traffic data as well as data occurrence data of porthole. To assess the influences which are PAR(Pitch Adjusting Rate) and HMCR(Harmony Memory Considering Rate), we determined suitability by changing the values. In the process of the determining a predictive model, the predictive model composed Training data (2011, 2012 and 2013yrs data). To determine the suitability of the model, we have utilized Testing Set (2009 and 2010 yrs data). The suitability of the basic prediction model has been from RMSE(Root Mean Squared Error), MAE(Mean Absolute Error) and Coefficient of determination.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.141-148
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• 2011

City of Incheon opened rapid transit(urban railroad line 1) in 1999 in order to relieve traffic congestion on major arterials. This research analyzed urban space changes of each station influenced area by examining the changes of residents, industrial employees, and floor area ratios before and after the open of Incheon urban railroad line 1. Analysis of variance was applied to change of station influence area. This research found out that the types of central business and agricultural station influence area had been changed significantly after the open of urban railroad. However, the residential, semi-residential, and suburban type of station influence area had not been changed after the open of urban railroad. Thus, it is necessary to provide diverse facilities for inducing residents and employees to the station influence area of residential and semi-residential. Furthermore, the suburban type of station influence area which is difficult to develop naturally by the law of Green Belt is needed to develop station influence area simultaneously with the construction of a station building.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.511-524
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• 2017

This study aims to evaluate the availability of ABM (Active-Based Model), FEATHERS, as a policy evaluation tool. To achieve the goal, scenario analysis on flexible working policy was conducted to measure its impact on activity-travel behavior. As a consequence, there seems no significant change in worker's daily life, other than mitigating traffic congestion due to decreasing commuting travel in the rush hour. The result of VKT (vehicle kilometers traveled) shows an opposite pattern according to given household/individual constraints. The scenario analysis on telecommuting indicates a decreasing trend in both travel frequency and distance because of the diminished number of commuting trips. As the activity space of telecommuters is shifted to a residential area, there are more short-distance trips by using non-motorized transport, which leads to decrease in VKT (using a private vehicle). Thus, the sensitivity of VKT by population groups varies due to transport mode shift (between personal and another mode) and growing non-work trips (using a private mode). This study found few things. First, it is necessary to evaluate the details of policy impact by population groups since it can be varied depending on household/individual characteristics. Second, the case study shows a promising performance of ABM as policy measurement that provides reality in policy evaluation. Third, ABM allows us to do more accurate analysis (i.e. time-series analysis by population groups) of policy assessment than those of FSM (Four-Step Model). Lastly, a further effort in data collection, literature review, and expert survey should be made to enhance the accuracy and confidence of future research.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.151-200
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• 2018

At the Davos Forum in 2016, the Fourth Industrial Revolution, a reference to cloud Schwab, is dramatically changing our lives, and at its height, self-driving cars are emerging as the talk of the day. But there are still many hurdles to overcome before the nation can successfully introduce and establish self-driving cars. In particular, it is necessary to change the paradigm of the legal system centered on human beings to one that includes artificial intelligence. The stable operation of the self-driving car era requires drastic changes to the people-centric legislation system. That is, it is necessary to collect information on the total number of drivers of self-driving cars (what is available), general vehicles on general roads, civil and criminal liability issues in the event of traffic accidents, and collection of insurance problems concerning autonomous driving vehicles. In this study, a separate bill was proposed to address the various legal issues arising from the operation of self-driving cars from a legislative perspective by considering the domestic laws related to road transport, the current state of legislation on foreign soil and legal issues related to self-driving cars.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.124-140
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• 2017

There exists a limitation to provide proper advance information for safe maneuvering through guidance and caution signs in freeway mobile work zones, unlike fixed work zones. Although a work-protection vehicle is currently deployed at the rear of the work vehicle, more active countermeasures to prevent crashes are required. The purpose of this study was to propose a method to evaluate the safety in mobile work zones and to present effective countermeasures. Driving simulation experiments were conducted to analyze characteristics of driver's behavior in mobile and fixed work zones. Safety distance index (SDI) based on the comparison of stopping distances of a work-protection vehicle and a following subject vehicle was used to evaluate traffic safety. More dangerous driving behavior was observed in the mobile work zone. Especially, it was identified that the lane-change of vehicles following the work- protection vehicle was late. Therefore, it is necessary to actively introduce methods to provide warning information so that the driver can recognize the work-protection vehicle in advance and carry out appropriate evasive maneuvers.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.1
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• pp.39-53
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• 2018

Generally, the total ventilation resistance coefficient in a tunnel consists of inlet/outlet loss coefficient, wall friction coefficient, and other loss coefficient caused by sudden expansion and contraction of cross-section, etc. For the tunnel before opening, when the running ventilation fan is stopped, the wind speed in the tunnel is reduced by the total ventilation resistance drag. The velocity decay method is comparatively stable and easy to estimate the wall friction coefficient in the pre-opening tunnel. However, the existing study reported that when the converging wind speed is a negative value after the ventilation fan stops, it is difficult to estimate the wall friction coefficient according to the velocity decay method. On the other hand, for the operating tunnel in which the piston effect acts, a more complex process is performed; however, a reasonable wall friction coefficient can be estimated. This paper aims at suggesting a method to minimize the measurement variables of the piston effect and reviewing a method that can be applied to the operating tunnel. Also, in this study, a new method has been developed, which enables to calculate an variation of the piston effect if the piston effect is constant with a sudden change of external natural wind occurring while the wind speed in the tunnel decreases after the ventilation fan stops, and a programming logic has been also developed, which enables dynamic simulation analysis in order to estimate the wall friction coefficient in a tunnel.

• Smart Media Journal
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• v.4 no.4
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• pp.24-32
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• 2015

Recently, SDN is actively used as datacenter networks and gradually increase its applied areas. Along with this change of networking environment, research of deploying network security systems on SDN becomes highlighted. Especially, systems for detecting network flooding attacks by monitoring every packets through ports of OpenFlow switches have been proposed. However, because of the centralized management of a SDN controller which manage multiple switches, it may be substantial overhead that the attack detection system continuously monitors all the flows. In this paper, a sampling based network flooding attack detection and prevention system is proposed to reduce the overhead of monitoring packets and to achieve reasonable functionality of attack detection and prevention. The proposed system periodically takes sample packets of network flows with the given sampling conditions, analyzes the sampled packets to detect network flooding attacks, and block the attack flows actively by managing the flow entries in OpenFlow switches. As network traffic sampler, sFlow agent is used, and snort, an opensource IDS, is used to detect network flooding attack from the sampled packets. For active prevention of the detected attacks, an OpenDaylight application is developed and applied. The proposed system is evaluated on the local testbed composed with multiple OVSes (Open Virtual Switch), and the performance and overhead of the proposed system under various sampling condition is analyzed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.859-867
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• 2019

Severe weather conditions, such as heavy snowfall, rain, heatwave, etc., may affect travel behaviors of people and finally change traffic patterns in transportation networks. To deal with those changes and prevent any negative impacts on the transportation system, understanding those impacts of severe weather conditions on the travel patterns is one of the critical issues in the transportation fields. Hence, this study has focused on the impacts of a weather condition on travel patterns of public transportations, especially when a heavy snowfall which is one of the most critical weather conditions. First, this study has figured out the most significant weather condition affecting changes of public transport ridership using weather information, card data for public transportation, mobile phone data; and then, developed a decision-tree model to determine complex inter-relations between various factors such as socio-economic indicators, transportation-related information, etc. As a result, the trip generation of public transportations in Seoul during a heavy snowfall is mostly related to average access times to subway stations by walk and the number of available parking lots and spaces. Meanwhile, the trip attraction is more related to business and employment densities in that destination.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.21-28
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• 2020

Technological advancements such as artificial intelligence, robots, and big data are revolutionizing the entire society. In this paper, we analyzed preliminary teachers' daily internet/gaming/smartphone habit and the difference between preference factors in gender and diagnosis group in the situation of ethical dilemma in driverless cars. The result shows most of the male students are in high risk group of daily internet/gaming usage, and male students tend to be more immersed in games compared to female students, which negatively affects their daily lives. Students who have at least one of the daily internet/gaming/smartphone habits are more likely to be classified as high-risk group in all three of daily internet/gaming/smartphone habit. Fortunately, the students perceived themselves addicted and wanted change their habits. An analysis by a moral machine of these students tells that there is no significant difference in preference between male and female students and among diagnosis groups. However, specifically in the ethical dilemma of driverless cars, all the groups of male, female, normal, high-risk showed they have priority in pedestrians over drivers, a large number of people over small, and people who obey traffic rules over who do not. The tendency was pronounced in female group and high-risk students prioritized people who are older and in lower social status.