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토픽모델링 기반의 국내외 미래 자동차 연구동향 비교 분석: CASE 키워드 중심으로.
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- Author(s): 정호정1 ; 김건욱2 ; 김나경3 ; 장원준4 ; 정원웅4 ; 박대영5
- Source:
Journal of Digital Convergence. 2022, Vol. 20 Issue 5, p463-476. 14p.
- Subject Terms:
- Additional Information
- Alternate Title:
Analysis of domestic and foreign future automobile research trends based on topic modeling.
- Abstract:
After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry. [ABSTRACT FROM AUTHOR]
- Abstract:
Copyright of Journal of Digital Convergence is the property of Society of Digital Policy & Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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