• Journal of Korean Port Research
• /
• v.14 no.3
• /
• pp.269-278
• /
• 2000

Today the pedestrian-related problems a key subject requiring the attention of the traffic engineers for improving the transportation system. Particularly in urban and CBD locations, the pedestrian presents an element of sharp conflict with vehicular traffic. Therefore pedestrian movements must be studied for the purpose of providing guideline for the design and operation of walking transportation systems. This paper is to address the characteristics of walking transportation in a big city. Especially the focuses are emphasized on the ratio occupied by pedestrian traffic among the whole unlinked trips in a city and walking time. The data for analysis are collected in Seoul metropolitan city through sampling 1,006 citizens. Compared with other similar research works this paper utilized diversified tools to acquire more useful results. Finally, policy directions for pedestrian environment improvement were suggested.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.07a
• /
• pp.461-462
• /
• 2014

모든 산업에서 정보화의 중요성을 언급하고 있는 것처럼 농업부문에서도 농업정보화는 우리나라의 농업이 세계의 농업 속에서 살아남을 수 있는 길이다. 미국과 EU를 비롯한 여러 국가들과의 FTA 체결 등으로 "21C의 농업은 국경이 무너졌다"는 말이 나올 정도로 기존의 농업처럼 국가의 보호아래 있는 것이 아니라 다른 국가의 농민들과 경쟁을 해야 하는 농업으로 바뀌었다. 우리 나라 농업도 정보자원을 증대시켜 토지, 노동, 자본 등 전통적 자원의 열위를 극복하여야 하며, 이를 위해 정부는 물론 농업의 주체들이 모두 정보화에 적극적으로 나서서 농업을 정보집약 산업으로 육성시켜야 한다는 필요성을 느끼고 있으며 계속적인 정보화사업을 추진하고 있다. 본 연구에서는 기존의 농업용 S/W와 빅데이터 등의 최신 ICT 기술의 연계 및 활용 방안을 제시하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.545-548
• /
• 2016

Big data is all data generated in the digital environment which has a variety of large and a short life cycle. The amount and type of data are becoming more and more produced on a larger scale, as a smart phone and the internet are popular, and consequently it has been converted into time for users to take advantage and extract only the valuable and useful data from the generated big data. Big data can also be applied to the medical industry and health sectors. It has created the synergy to be fused with ICT such as IoT, smart healthcare, and so on. However, there will be challenges like data security in order securely to use a meaningful and useful vast amounts of data. In this study, we analyze the future prospects of the healthcare, applications and issues of medical big data, and the expected challenges.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.4
• /
• pp.99-111
• /
• 2018

Service areas in expressways are very important facilities in terms of efficient expressway operation and the convenience of users. It needs a traffic management strategy to inform drivers in advance about congestion in service areas so as to distribute users of service areas. But due to the lack of sensors and data on numbers of people in the service areas, congestion in service areas had not been measured and managed appropriately. In this study, a congestion index for service areas was developed using telecommunication floating population big data. Two alternative indices (i.e., density of service areas and floating population V/c of service areas) were developed. Finally, the floating population V/c of service areas was selected as a congestion index for service areas for reasons of the ease of understanding and comparison.

• Journal of the Korea Safety Management & Science
• /
• v.25 no.2
• /
• pp.159-165
• /
• 2023

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

• The Journal of Information Systems
• /
• v.32 no.2
• /
• pp.137-152
• /
• 2023

Purpose The purpose of this study is to analyze big data related to DURIBAL from the operation of taxis reserved for the disabled to identify the issues and suggest solutions. ESG management should be translated into "environmental factors, social responsibilities, and transparent management." Therefore, the current study used Big Data analysis to analyze the factors affecting the standby of taxis reserved for the disabled and relevant problems for implications on convenience of social weak. Design/methodology/approach The analysis method used R, Excel, Power BI, QGIS, and SPSS. We proposed several suggestions included problems with managing cancellation data, minimization of dark data, needs to develop an integrated database for scattered data, and system upgrades for additional analysis. Findings The results showed that the total duration of standby was 34 minutes 29 seconds. The reasons for cancellation data were mostly use of other modes of transportation or delayed arrival. The study suggests development of an integrated database for scattered data. Finally, follow-up studies may discuss government-initiated big data analysis to comparatively analyze the use of taxis reserved for the disabled nationwide for new social value.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.2
• /
• pp.529-534
• /
• 2020

The development of civilization is in line with evolution of cities and transportation technology caused by industrialization. Up to now, a city has been developed owing to transportation cost reduction and needs for land utilization as a limited core business district. Continuous increase of urban population density has accompanied by lots of problems socioeconomically such as rise of land value, traffic congestion, gap between the rich and poor, air pollution, etc. Those issues are difficult to be solved in existing city ecosystem. However, a clue for solving the problems could be found in there. The design of Seoul mid-night bus route was from analysis of movement of people in the rural area by using ICT so that a city ecosystem should be firstly analyzed for solving rural issues. If the cause of those is found, big data platform construction is required to raise the life quality of citizen and the problems could be solved. Big data should be located in the middle of the platform connected with every element of city based on ICT for real-time collection, analysis and application. This paper addresses construction of big data platform and its application for sustainable smart city.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.16 no.4
• /
• pp.463-469
• /
• 2016

This paper presents a compression-friendly low power test scheme in EDT environment. The proposed approach exploits scan slices reusing to reduce the switching activity during shifting for test scheme based on linear decompressor. To avoid the impact on encoding efficiency from resulting control data, a counter is utilized to generate control signals. Experimental results obtained for some larger ISCAS'89 and ITC'99 benchmark circuits illustrate that the proposed test application scheme can improve significantly the encoding efficiency of linear decompressor.

• Journal of KIBIM
• /
• v.13 no.4
• /
• pp.106-114
• /
• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.6
• /
• pp.157-167
• /
• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.