Shared mobility is a service that allows users to share various transportation modes and use them with reservations when necessary. It started with private automotive car-sharing and ride-sharing services. Currently, it operates on a wider range, including personal mobility devices such as electric bicycles and scooters. The purpose of this study is to derive a direction for providing future shared mobility services through analysis of factors affecting the usage intention of both current and prospective users. The survey targets 753 citizens living in Gyeonggi Province, Korea. The survey period is from February 12, 2020, to February 26, 2020. In this study, a logistic regression analysis is conducted to investigate the factors affecting the use intention of shared mobility. The analysis results show that gender, car ownership, and education, among variables reflecting socio-demographic characteristics, have significant effects on intention to use shared mobility. Moreover, we find that experience factors, including mainly used transportation modes, ownership of shared mobility device, past experience in similar services, satisfaction of existing shared mobility services, and distance from the home to the nearest bus stop, are also statistically influential. The analysis results are expected to lay the foundation for the introduction of shared mobility services and can be used as data for planning smart mobility services in the future.

中文翻译：

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使用共享出行服务意向调查数据分析

共享出行是一项服务，可让用户共享各种交通方式，并在需要时使用它们进行预订。它从私人汽车共享汽车和拼车服务开始。目前，它的运行范围更广，包括诸如电动自行车和踏板车之类的个人移动设备。这项研究的目的是通过分析影响当前和潜在用户使用意愿的因素，得出提供未来共享出行服务的方向。该调查针对居住在韩国京畿道的753名市民。调查期为2020年2月12日至2020年2月26日。在本研究中，进行了逻辑回归分析以研究影响共享出行的使用意图的因素。分析结果表明，性别，汽车拥有量和受教育程度，在反映社会人口特征的变量中，对使用共享出行的意愿有重大影响。此外，我们发现经验因素，包括主要使用的交通方式，共享出行设备的所有权，过去在类似服务中的经验，对现有共享出行服务的满意度以及从家到最近的公交车站的距离，也具有统计学影响。预计分析结果将为引入共享移动服务奠定基础，并可在将来用作计划智能移动服务的数据。从统计上讲，过去在类似服务中的经验，对现有共享出行服务的满意度以及从房屋到最近的公交车站的距离也是有影响的。预计分析结果将为引入共享移动服务奠定基础，并可在将来用作计划智能移动服务的数据。从统计上讲，过去在类似服务中的经验，对现有共享出行服务的满意度以及从房屋到最近的公交车站的距离也是有影响的。预计分析结果将为引入共享移动服务奠定基础，并可在将来用作计划智能移动服务的数据。