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

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.