• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.11a
• /
• pp.302-304
• /
• 2022

항만 서비스 의미가 선박의 양적하뿐만 아니라 육로를 통해 반출입되는트레일러의 하역도 포함 디지털프랜스포메이션가속화에 따라 IoT, 빅데이타, 인공지능을 활용한 항만의 생산성 증대 방안으로 선박의 TAT뿐만 아니라 항만 반출입트럭TAT 감축의 중요성도 같이 높아지고 있음 컨테이너 이송 트럭의 TAT에 대한 정확한 측정과 TAT에 영향을 미치는 요인의 규명은 컨테이너 운송에 중요한 역할을 한다고 할 수 있음 따라서 본 연구의 목적은 IoT 기술로 수집된 빅데이터를 활용해 실질적인 차량 반출입시간을 분석한 새로운 항만 반출입차량 TAT 데이터와 기후, 부두 실적, 기항 선박 사이즈, 시간대 등 다양한 항만물류빅데이터를이용하여 항만 반출입차량 TAT에 영향을 미치는 요인을 분석하고 나아가 항만물류빅데이터분석을 위한 빅데이터 수집 방법을 연구하는데 목적이 있음

• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.129-140
• /
• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.07a
• /
• pp.287-288
• /
• 2019

본 논문에서는 대용량 화물 DTG 데이터 분석을 위한 빅데이터 플랫폼을 설계 및 구현한다. DTG(digital tacho graph)는 차량운행기록을 실시간으로 저장하는 장치로서, 차량의 GPS, 속도, RPM, 제동유무, 이동거리 등 차량운행 관련 데이터가 1초 단위로 기록된다. 차량 운행 패턴 및 분석을 하기 위해서는 DTG 데이터의 빠른 처리가 필수적이며, 특히 대용량 DTG 데이터를 가공 및 변환하기 위해서는 별도의 플랫폼이 필요하다. 본 논문에서는 오픈소스 기반의 빅데이터 프레임워크인 스파크(Spark)를 이용하여 대용량 화물 DTG 데이터의 전처리 플랫폼을 구현하였다. 실제 대용량 화물 DTG 데이터를 대상으로 데이터를 변환 및 지도상에 표현해 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.548-551
• /
• 2018

Most vehicles are in the form of maintenance when a problem occurs by the user himself or herself. During maintenance, users are not able to operate the car while it is being serviced, and if the target vehicle is a revenue-generating vehicle, they will have to bear economic losses. Collecting vehicle information in real time, identifying problems that could arise with a vehicle based on the collected big data and providing advance service rather than after-sales service can help secure vehicle operation and reduce economic loss. Thus, in this thesis, a platform was designed to design IoT-based gateways, collect real-time vehicle information, and organize big data to provide vehicle information in real time.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.2
• /
• pp.74-84
• /
• 2022

Vehicle average speed information which measured at a point or a short section has a problem in that it cannot accurately provide the speed changes on an actual highway. In this study, segment separation method based on vehicle big data for accurate micro-speed estimation is proposed. In this study, to find the point where the speed deviation occurs using location-based individual vehicle big data, time and space mean speed functions were used. Next, points being changed micro-scale speed are classified through gradual segment separation based on geohash. By the comparative evaluation for the results, this study presents that the link-based speed is could not represent accurate speed for micro-scale segments.

• Journal of Digital Contents Society
• /
• v.14 no.4
• /
• pp.447-454
• /
• 2013

As the surveillance CCTVs are increasing every year, big data image processing for the CCTV image data has become a hot issue. In this paper, we propose a Hadoop-based big data image processing technique to recognize a vehicle number from a large amount of automatic number plate images taken from CCTVs. We also implement the vehicle route tracking system that displays the moving path of the searched vehicle on Google Maps with the related information together. In order to evaluate the performance we compare and analysis the vehicle number recognition time for a lot of CCTV image data in Hadoop and the single PC environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.3
• /
• pp.105-114
• /
• 2019

As the Industrial Revolution progressed, Big Data technologies were used to develop a system that instantly identified the consequences of older vehicles using mobile devices. First, data from the vehicle was collected using the OBD2 sensor, and the data collected was stored in the raspberry pie, giving it the same situation that the raspberry pie was driving. In the event that vehicle data is generated, the data is collected in real time, stored in multiple nodes, and visualized and printed based on the processed, refined, processed and processed data. We can use Big Data in this process and quickly process vehicle data to identify it effectively through mobile devices.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.12
• /
• pp.67-74
• /
• 2019

In this paper, we design and implement an analytics platform based on bulk cargo DTG data for crackdown on overloaded trucks. DTG(digital tachograph) is a device that stores the driving record in real time; that is, it is a device that records the vehicle driving related data such as GPS, speed, RPM, braking, and moving distance of the vehicle in one second unit. The fast processing of DTG data is essential for finding vehicle driving patterns and analytics. In particular, a big data analytics platform is required for preprocessing and converting large amounts of DTG data. In this paper, we implement a big data analytics framework based on cargo DTG data using Spark, which is an open source-based big data framework for crackdown on overloaded trucks. As the result of implementation, our proposed platform converts real large cargo DTG data sets into GIS data, and these are visualized by a map. It also recommends crackdown points.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.107-117
• /
• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1794-1799
• /
• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.