• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.1219-1221
• /
• 2008

본 논문에서는 막대한 양의 위치 정보가 축적되는 차량 텔레매틱스 시스템을 대상으로 이동 이력 데이터에 대한 효과적인 분석을 위하여 이동 객체들의 궤적과 위치 변화를 시간적인 흐름에 따라 디지털 맵에 표현하는 인터페이스를 설계하고 구현하였다. 분석기 모듈은 쓰레드로 구현되어 윈도우즈 운영체제의 쓰레드 제어함수에 의해 분석 모듈도 같이 수행이 제어될 수 있으며 상용 디지털 맵인 알맵에 기반하여 이의 API에 따라 지도 인터페이스를 구현하였다. 또한 도로상에서의 분석을 위해 도로 네트워크 상에서의 표현도 구현하였다. 본 논문에서 구현된 분석 인터페이스의 구조는 쓰레드, 디지털 맵 등에 대한 요소들을 적절히 결합하여 새로운 Add-in 분석 기능을 추가할 수 있도록 한다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.1
• /
• pp.75-84
• /
• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.5
• /
• pp.72-84
• /
• 2017

Together with the Big Data of the 4th Industrial Revolution, the traffic information system has been changed to an section detection system by the point detection system. With DTG(Digital Tachograph) data based on Global Navigation Satellite System, the properties of raw data and data according to processing step were examined. We identified the vehicle trajectory, the link travel time of individual vehicle, and the link average travel time which are generated according to the processing step. In this paper, we proposed a application method for traffic management as characteristics of processing data. We selected the historical data considering the data management status of the center and the availability at the present time. We proposed a method to generate the Travel Time Index with historical link average travel time which can be collected all the time with wide range. We propose a method to monitor the traffic congestion using the Travel Time Index, and analyze the case of intersections when the traffic operation method changed. At the same time, the current situation which makes it difficult to fully utilize DTG data are suggested as limitations.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.3
• /
• pp.45-54
• /
• 2008

This paper designs and Implements a connectivity analyzer for the hybrid vehicular network based on the real-life movement history data achieved from the Taxi telematics system currently in operation, aiming at providing a useful guideline and information to build a telematics network. The simulator traces the location of each vehicle, sets the vehicle type, either gateway or normal, decides whether it can be connected to a mobile gateway, keeps track of status of the vehicle, and calculates the duration of disconnected state. With this analysis considering the transmission range and gateway ratio, we can decide the cost-effective number of mobile gateways having both cellular and ad-hoc network interfaces, and buffer space requirement based on the measured disconnection time and message generation ratio.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.4
• /
• pp.55-66
• /
• 2020

This study developed four algorithms and their associated indices that can quantify and qualify road hazards along roadways. Initially, relevant raw data can be collected from commercial vehicles by camera and DTG. Well-processed data, such as potholes, road freezing, and fog, can be generated from the Integrated management system. Road hazard algorithms combine these data with road inventory data in the Data Sharing Platform. Depending on well-processed data, four different road hazard algorithms and their associated indices were developed. To test the algorithms, an experimental plan based on passive DTG attached in probe vehicles was performed at two different test locations. Selection of the test routes was based on historical data. Although there were limitations using random data for commercial vehicles, hazardous roadways sections, such as fog, road freezing, and potholes, were generated based on actual historical data. As a result, no algorithm error was found in the entire test. Because this study provides road hazard information according to a section, not a point, it can be practically helpful to road users as well as road agencies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.337-339
• /
• 2015

As much interest recently location based service, a study to analyze the movement patterns of the users from getting a lot of large amounts of data collected from a GPS installed in the smart device. Also it is recorded in the log file in the form of day-to-day personal computer, the development of a variety of a smart phone, a black box, and navigation. This data is collected by the user have been developed a variety of personalized services. In this paper, using the black box camera, such as the vehicle to form a social network platform that broadcasts real-time traffic utilization real-time video information.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.4 no.3 s.8
• /
• pp.33-43
• /
• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.7
• /
• pp.325-330
• /
• 2017

Bluetooth 4.0 is a technology suitable for the Internet of things that is used for communication between various devices. This technology is suitable for developing a service by combining with automobiles. In this study, a security authentication system was designed by linking Bluetooth 4.0 technology and a vehicle system as an implementation example of an object internet service. A procedure was designed for security authentication and an authentication method is proposed using a data server. When the security authentication function is provided, various additional services can be developed using the information collection function of the risk notification and user action history. In addition, BLE (Bluetooth Low Energy) technology, which is a wireless communication technology that enables low-power communication and low-power communication in the process of the standardization and development of Bluetooth technology and technology, improves the battery life through the use of RFID or NFC This study expanded the range possible. The security service can be extended by expanding the scope of authentication by the contactless type. Using the proposed system, a customized service can be provided while overcoming the problems of an existing radio frequency (RF)-based system, portability, and battery usage problem.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.3
• /
• pp.66-77
• /
• 2014

UTIS(Urban Traffic Information System) directly collects link travel time in urban area by using probe vehicles. Therefore it can estimate more accurate link travel speed compared to other traffic detection systems. However, UTIS includes some missing data caused by the lack of probe vehicles and RSEs on road network, system failures, and other factors. In this study, we suggest a new model, based on k-NN algorithm, for imputing missing data to provide more accurate travel time information. New imputation model is an adaptive k-NN which can flexibly adjust the number of nearest neighbors(NN) depending on the distribution of candidate objects. The evaluation result indicates that the new model successfully imputed missing speed data and significantly reduced the imputation error as compared with other models(ARIMA and etc). We have a plan to use the new imputation model improving traffic information service by applying UTIS Central Traffic Information Center.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.47-73
• /
• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.