Mobility as a service (MaaS) integrates various transportation modalities and connects these modalities to enable users to travel from departure points to destination points. The objectives of this study were to analyze the factors influencing the intention to use MaaS and determine a direction for the implementation of this service. Hence, this study utilized an ordered probit model and marginal effect analysis. Specifically, a survey was conducted based on citizens living in Gyeonggi-do, South Korea, to prepare logical MaaS operation measures customized in this province. Subsequently, variables related to MaaS were classified, and statistical analysis was performed based on these variables. The need for integrated transportation service, intention to use shared mobility, gender, household income, and region are found to have significant effects. Moreover, demographic groups with high levels of intention to use MaaS were identified. Finally, the intention to use MaaS was found to be mainly affected by whether public transportation was the primary means of transportation or whether the subjects spent a comparatively long time commuting. It is expected that these findings will serve as the basis for MaaS implementation in the future and will also be used as fundamental data for transportation service planning.

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.