Deriving Mobility Service Policy Issues Based on Text Mining: A Case Study of Gyeonggi Province in South Korea

Younghoon Seo, Donghyun Lim, Woongbee Son, Yeongmin Kwon, Junghwa Kim and Hyungjoo Kim
Additional contact information
Younghoon Seo: Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea
Donghyun Lim: Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea
Woongbee Son: Gyeonggi Autonomous Driving Center, Advanced Institute of Convergence Technology, Seongnam 13449, Korea
Yeongmin Kwon: Policy Research Team, Incheon International Airport Corporation, Incheon 22382, Korea
Junghwa Kim: Department of Urban and Transportation Engineering, Kyonggi University, Suwon 16227, Korea
Hyungjoo Kim: Intelligent Transportation System Lab, Advanced Institute of Convergence Technology, Suwon 16229, Korea

Sustainability, 2020, vol. 12, issue 24, 1-20

Abstract: Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

Keywords: mobility service; text mining; TF-IDF; clustering; Gyeonggi Province; expert survey (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/24/10482/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/24/10482/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:24:p:10482-:d:462421

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().