• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.759-766
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• 2008

Road classification system is the first step for determining the road function and design standards. Currently, roads are classified by various indices such as road location and function. In this study, we classify road using various traffic indices as well as to identify traffic characteristics for each type of road. To accomplish the objectives, mixture model was applied for classifying road and analyzing traffic characteristics using traffic data that observed at permanent traffic count stations. A total of 8 variables were applied: annual average daily traffic(AADT), $K_{30}$ coefficient, heavy vehicle proportion, day volume proportion, peak hour volume proportion, sunday coefficient, vacation coefficient, and coefficient of variation(COV). A total of 350 permanent traffic count points were categorized into three groups : Group I (Urban road), Group II (Rural road), and Group III (Recreational road). AADT were 30,000 for urban, 16,000 for rural, and 5,000 for recreational road. Group III was typical recreational road showing higher average daily traffic volume during Sunday and vacational periods. Group I showed AM peak and PM peak, while group II and group III did not show AM peak and PM peak.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.847-856
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• 2016

Recently, with a high level of economic growth, rapid urbanization, population, environment and housing problems were accompanied in Korea. In particular, the traffic problem has become a serious social problem. As the current transportation policy has been carried out, concentrating on traffic flow, in 2015, death rate for pedestrians while walking (1,795 persons) is 38.8% compared to entire death rate in car accident (4,621 persons), so there is need to solve it. Although, crosswalk should make pedestrian cross it safely, it has been made on the basis of the width of road without exact standard for current width of the crosswalk and the location of stop line. Moreover, in the area around many campuses or commercial facilities, crosswalks are set with not considering pedestrian passage, but designed uniformly. Therefore, the purpose of this study is to estimate reasonable dimension of crosswalk considering pedestrian traffic and walking speed and it makes the accident rate lower in the crosswalk, which has a lot of problems including decisions of vehicle traffic signal time, lack of pedestrian's signal timing, pedestrian's crossing of long-distance. The following are the methodology of the study. Firstly, for crosswalk calculation of specifications, examination relating existing regulations and researches dealing with crosswalk, pedestrians and stop line is needed. After analyzing problems of current width of crosswalk and stop line, present the methodology to calculation of specifications and basing on these things, calculation of specifications for crosswalk will be decided. In conclusion, the calculation of specification and improvement of stop line for crosswalk laid out in this study are expected to be utilized as base data in case of establishing relevant safety facilities and standards.

• Journal of IKEEE
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• v.23 no.4
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• pp.1461-1464
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• 2019

In this paper, we propose the development of embedded board for integrated radiation exposure protection fireman's life-saving alarm capable of location tracking and radiation measurement. The proposed techniques consist of signal processing unit, communication unit, power unit, main control unit. Signal processing units apply shielding design, noise reduction technology and electromagnetic wave subtraction technology. The communication unit is designed to communicate using the wifi method. In the main control unit, power consumption is reduced to a minimum, and a high performance system is formed through small, high density and low heat generation. The proposed techniques are equipment operated by exposure to poor conditions, such as disaster and fire sites, so they are designed and manufactured for external appearance considering waterproof and thermal endurance. The proposed techniques were tested by an authorized testing agency to determine the effectiveness of embedded board. The waterproof grade has achieved the IP67 rating, which can maintain stable performance even when flooded with water at the disaster site due to the nature of the fireman's equipment. The operating temperature was measured in the range of -10℃ to 50℃ to cope with a wide range of environmental changes at the disaster site. The battery life was measured to be available 144 hours after a single charge to cope with emergency disasters such as a collapse accident. The maximum communication distance, including the PCB, was measured to operate at 54.2 meters, a range wider than the existing 50 meters, at a straight line with the command-and-control vehicle in the event of a disaster. Therefore, the effectiveness of embedded board for embedded board for integrated radiation exposure protection fireman's life-saving alarm has been demonstrated.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.44-63
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• 2000

Situated close to Heathrow Airport, and adjacent to the M4 and M25 Motorways, the site at Axis Park is considered a prime location for business in the UK. In consequnce two of the UK's major property development companies, MEPC and Redrew Homes sought the expertise of Intergeo to remediate the contaminated former industrial site prior to its development. Industrial use of the twenty-six hectare site, started in 1936, when Hawker Aircraft commence aircraft manufacture. In 1963 the Firestone Tyre and Rubber Company purchased part of the site. Ford commenced vehicle production at the site in the mid-1970's and production was continued by Iveco Ford from 1986 to the plant's decommissioning in 1997. Geologically the site is underlain by sand and gravel, deposited in prehistory by the River Thames, with London Clay at around 6m depth. The level of groundwater fluctuates seasonally at around 2.5m depth, moving slowly southwest towards local streams and watercourses. A phased investigation of the site was undertaken, which culminated in the extensive site investigation undertaken by Intergeo in 1998. In total 50 boreholes, 90 probeholes and 60 trial pits were used to investigate the site and around 4000 solid and 1300 liquid samples were tested in the laboratory for chemical substances. The investigations identified total petroleum hydrocarbons in the soil up to 25, 000mg/kg. Diesel oil, with some lubricating oil were the main components. Volatile organic compounds were identified in the groundwater in excess of 10mg/l. Specific substances included trichloromethane, trichloromethane and tetrachloroethene. Both the oil and volatile compounds were widely spread across the site, The specific substances identified could be traced back to industrial processes used at one or other dates in the sites history Slightly elevated levels of toxic metals and polycyclic aromatic hydrocarbons were also identified locally. Prior to remediation of the site and throughout its progress, extensive liaison with the regulatory authorities and the client's professional representatives was required. In addition to meetings, numerous technical documents detailing methods and health and safety issues were required in order to comply with UK environmental and safety legislation. After initially considering a range of options to undertake remediation, the following three main techniques were selected: ex-situ bioremediation of hydrocarbon contaminated soils, skimming of free floating hydrocarbon product from the water surface at wells and excavations and air stripping of volatile organic compounds from groundwater recovered from wells. The achievements were as follows: 1) 350, 000m3 of soil was excavated and 112, 000m3 of sand and gravel was processed to remove gravel and cobble sized particles; 2) 53, 000m3 of hydrocarbon contaminated soil was bioremediated in windrows ; 3) 7000m3 of groundwater was processed by skimming to remove free floating Product; 4) 196, 000m3 of groundwater was Processed by air stripping to remove volatile organic compounds. Only 1000m3 of soil left the site for disposal in licensed waste facilities Given the costs of disposal in the UK, the selected methods represented a considerable cost saving to the Clients. All other soil was engineered back into the ground to a precise geotechnical specification. The following objective levels were achieved across the site 1) By a Risk Based Corrective Action (RBCA) methodology it was demonstrated that soil with less that 1000mg/kg total petroleum hydrocarbons did not pose a hazard to health or water resources and therefore, could remain insitu; 2) Soils destined for the residential areas of the site were remediated to 250mg/kg total petroleum hydrocarbons; in the industrial areas 500mg/kg was proven acceptable. 3) Hydrocarbons in groundwater were remediated to below the Dutch Intervegtion Level of 0.6mg/1; 4) Volatile organic compounds/BTEX group substances were reduced to below the Dutch Intervention Levels; 5) Polycyclic aromatic hydrocarbons and metals were below Inter-departmental Committee for the Redevelopment of Contaminated Land guideline levels for intended enduse. In order to verify the qualify of the work 1500 chemical test results were submitted for the purpose of validation. Quality assurance checks were undertaken by independent consultants and at an independent laboratory selected by Intergeo. Long term monitoring of water quality was undertaken for a period of one year after remediation work had been completed. Both the regulatory authorities and Clients representatives endorsed the quality of remediation now completed at the site. Subsequent to completion of the remediation work Redrew Homes constructed a prestige housing development. The properties at "Belvedere Place" retailed at premium prices. On the MEPC site the Post Office, amongst others, has located a major sorting office for the London area. Exceptionally high standards of remediation, control and documentation were a requirement for the work undertaken here.aken here.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.60-70
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• 2019

Today, Deoksugung Doldam-gil, which is a well-known area in Seoul, has become a mixed place as many places reaching a critical age have been converted into parks. However, the previous research on the Deoksugung Doldam-gil was deficient in that the user, an essential variable, was not considered when assessing the place. Based on that, this study aims to analyze and interpret the perception of the places in Deoksugung Doldam-gil and to analyze factors to further enrich the place to visitors. According to the research, the representative idea of Deoksugung Doldam-gil is "the distance you want to go" and that has influencing factors, such as vehicle restrictions and the improvement of the walking environment. The analysis of classifying the variables that make up the perception of the place, physical environments, activities and meanings showed high awareness in, "streets of green (3.95)" and "stone walls of curves (3.88)." In the category of activities, "walking activities in the inner city (4.01)" and "love and romance (3.57)" were high. These results seem to reflect the spatial characteristics of the streets and the familiar image of the place were important. Five factors were extracted from the factor analysis to provide a more detailed understanding of the place perception, the correlation between each factor, and the place atmosphere of Deoksugung Doldam-gil. These factors confirmed a high correlation between 'green landscape' and 'historicity.' This can be attributed to the fact that the analysis reflects vital space, visual experience, and free walking conditions to be important, and these variables are present in urban parks. It also indicates the long-accumulated image and behavior near the site of Deoksugung Palace, including the historical and cultural heritage. It was confirmed that the factors related to the cognitive perception of Deoksugung Doldam-gil and the formation of the atmosphere of the place were strongly recognized. It found that there was a need to reflect the value and importance of 'green' in the future as culture or in the use of preservation and management related to heritage. This study presented a direction to be noted from the perspective of a user's place awareness, but considered only a fraction of the variables that affect the multidimensional sense of place and location recognition, and thus must be supplemented in the future.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.127-135
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• 2021

To respond to the threat of global warming, countries around the world are promoting the spread of renewable energy and reduction of carbon emissions. In accordance with the United Nation's Sustainable Development Goal to combat climate change and its impacts, global automakers are pushing for a full transition to electric vehicles within the next 10 years. Electric vehicles can be a useful means for reducing carbon emissions, but in order to reduce carbon generated in the stage of producing electricity for charging, a power generation system using eco-friendly renewable energy is required. In this study, we propose a smart electric mobility operating system integrated with off-grid solar power plants established in Tanzania, Africa. By applying smart monitoring and communication functions based on Arduino-based computing devices, information such as remaining battery capacity, battery status, location, speed, altitude, and road conditions of an electric vehicle or electric motorcycle is monitored. In addition, we present a scenario that communicates with the surrounding independent solar power plant infrastructure to predict the drivable distance and optimize the charging schedule and route to the destination. The feasibility of the proposed system was verified through test runs of electric motorcycles. In considering local environmental characteristics in Tanzania for the operation of the electric mobility system, factors such as eco-friendliness, economic feasibility, ease of operation, and compatibility should be weighed. The smart electric mobility operating system proposed in this study can be an important basis for implementing the SDGs' climate change response.