• Journal of the Society of Disaster Information
• /
• v.13 no.2
• /
• pp.199-212
• /
• 2017

Rapid roadway snow removal is significantly important due to difficult occurrence estimation of heavy snowfall disasters by global warming and climate change. Local governments of S. Korea have snow removal equipments and vehicles based on past experiences without considering snowfall and roadway characteristics. The objective of this research is to develop the demand estimation procedure for snow removal equipments and vehicles based on regional snowfall and roadway characteristics. This research first classifies regional snowfall characteristics using KMO's ten-year snowfall data. Second, roadway snow removal length is computed for local governments. Real possession data is compared with demand estimation of snow removal equipments & vehicles for each local government with roadway snow removal scenarios. Finally, required demands of snow removal equipments & vehicles are predicted by concerning regional snowfall amount and required snow removal hours. Results from this research are used for developing heavy snowfall disaster management policies for optimal demands and snow removal routes of 229 local governments.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.911-917
• /
• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.236-238
• /
• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1123-1130
• /
• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.8
• /
• pp.137-145
• /
• 2018

In this study, we categorized the subjective recognition of 20's young generation about virtual currency into a few meaningful types and analyzed the characteristics of each type using the Qmethodology. For the Qpopulation, we selected a total of 337 samples which were derived from the Internet search, in-depth interviews, and literature survey, chose the final 51 Qstatements, and performed the Q-classification for the 40 P samples of 20's, including university students. Then, we analyzed the results using the PC-QUANL program. From the analysis, we found meaningful differences between each recognition type, and, after classifying the recognition types into four, we named each of them as 'investment-vehicle' type, 'future-technology' type, 'kind of gambling-like plays' type, and 'foamy faddishness' type. We hope that this research result can contribute to follow-up research and social discussion as base materials.

• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.277-283
• /
• 2021

Traffic data by vehicle classification are important data used as basic data in various fields such as road and traffic design. Traffic data is collected through permanent and temporary surveys and is provided as an annual average daily traffic (AATD) in the statistical yearbook of road traffic. permanent surveys are collected through traffic collection equipment (AVC), and the AVC consists of a loop sensor that detects traffic volume and a piezo sensor that detects the number of axes. Due to the nature of the buried type of traffic collection equipment, missing data is generated due to failure of detection equipment. In the existing method, it is corrected through historical data and the trend of traffic around the point. However, this method has a disadvantage in that it does not reflect temporal and spatial characteristics and that the existing data used for correction may also be a correction value. In this study, we proposed a method to correct the missing traffic volume by calculating the axis correction coefficient through the accumulated number of axes acquired by using a piezo sensor that can detect the axis of the vehicle. This has the advantage of being able to reflect temporal and spatial characteristics, which are the limitations of the existing methods, and as a result of comparative evaluation, the error rate was derived lower than that of the existing methods. The traffic volume correction system using axis count is judged as a correction method applicable to the field system with a simple algorithm.

• International Journal of Highway Engineering
• /
• v.11 no.4
• /
• pp.69-78
• /
• 2009

Korea has achieved significant economic growth with building the Gyeongbu Expressway. As the number of new road construction projects has decreased, it becomes more important to maintain optimal status of the current road networks. One of the best ways to accomplish it is weight enforcement as active control measure of traffic load. This study is to develop High-speed Weigh-in-motion System in order to enhance efficiency of weight enforcement, and to analyze patterns of overloaded trucks on highways through the system. Furthermore, it is to review possibilities of developing overweight control system with application of the HS-WIM system. The HS-WIM system developed by this study consists of two sets of an axle load sensor, a loop sensor and a wandering sensor on each lane. A wandering sensor detects whether a travelling vehicle is off the lane or not with the function of checking the location of tire imprint. The sensor of the WIM system has better function of classifying types of vehicles than other existing systems by detecting wheel distance and tire type such as single or dual tire. As a result, its measurement errors regarding 12 types of vehicle classification are very low, which is an advantage of the sensor. The verification tests of the system under all conditions showed that the mean measurement errors of axle weight and gross axle weight were within 15 percent and 7 percent respectively. According to the WIM rate standard of the COST-323, the WIM system of this study is ranked at B(10). It means the system is appropriate for the purpose of design, maintenance and valuation of road infrastructure. The WIM system in testing a 5-axle cargo truck, the most frequently overloaded vehicle among 12 types of vehicles, is ranked at A(5) which means the system is available to control overloaded vehicles. In this case, the measurement errors of axle load and gross axle load were within 8 percent and 5 percent respectively. Weight analysis of all types of vehicles on highways showed that the most frequently overloaded vehicles were type 5, 6, 7 and 12 among 12 vehicle types. As a result, it is necessary to use more effective overweight enforcement system for vehicles which are seriously overloaded due to their lift axles. Traffic volume data depending upon vehicle types is basic information for road design and construction, maintenance, analysis of traffic flow, road policies as well as research.

• Journal of Advanced Navigation Technology
• /
• v.24 no.2
• /
• pp.73-84
• /
• 2020

To overcome ground congestions due to growing number of cars, a lot of companies have proposed personal aerial vehicle (PAV). Among PAV, electric vertical take-off and landing (eVTOL) aircrafts capable of vertical take-off and landing with electric power are drawing attention, and their configurations vary from multicopters to tilt ducted fans. This study tries to analyze the characteristics of each eVTOL design configurations. Parasite drag was calculated using component build up method for Vahana, Aurora, Volocopter representing each eVTOL PAV type of tilt-wing, compound, and multicopter. Wetted area and induced drag was calculated using OpenVSP and XFLR5 that are aircraft design and aerodynamic analysis software. The batteries used in the eVTOL PAV was assumed as Tesla 2170 batteries and flight ranges were calculated. Also, energy consumption and maximum flight time for the given mission profile including take-off and landing, cruising segments were compared for each eVTOL.

• Resources Recycling
• /
• v.10 no.4
• /
• pp.34-41
• /
• 2001

A research was performed to evaluate a use of shredder residue to currently dispose of at landfills. Laboratory analyses were conducted to determine especially the fuel characteristics of shredder residue. For this aim, shredder residue was classified by the particle size as well as by the type of material and the content of Cl, S, ash, and calorific value were determined. Due to the chlorinated plastic content of shredder residue, mean concentration of Cl was found to exceed 4wt% except one sample while that of S was ranged from 0.25 to 0.39 wt%. As far as calorific value was concemed, plastic was observed to be more than 10,000 kcal/kg while wood/paper and fiber accounted for approximately 4,000 kcal/kg. Shredder residue was found to contain varying trace amounts of metal elements, including Fe of 6∼8.5 wt%. Hg and Cr(VI) were not detected, however, while Cd was contained as small as 0.0004-0.0009 wt%.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.6
• /
• pp.250-265
• /
• 2022

Positive effects such as significantly reducing traffic accidents caused by human error can be expected by the introduction of Automated vehicles (AV). However, as new traffic safety issues are expected to occur in the future due to errors in H/W or S/W of autonomous vehicles and lack of its function, it is necessary to establish a scenario to evaluate the driving safety of AV. Therefore, in this study, functional scenario was developed to evaluate the driving safety of AV based on traffic accident data of the National Police Agency. Using the GIS program, QGIS, traffic accident data that occurred in the toll gate and ramp sections of expressway were extracted and accident summary items were checked to classify the types of accident. In addition, based on the results of accident type classification, functional scenario were developed that contains various dangerous situations in the tollgate and ramp sections.