Abstract
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.
Highlights
As negative impacts of autoownership have gradually increased, such as significant energy consumption, traffic congestion, inefficient land use, and excessive vehicle purchasing and operating costs, drivers have begun to look for alternatives [1]
KTX presented in Table 4 is an abbreviation of the โKorea Train expressโ operated by Korail, a Korean railway company. e use of car-sharing and shared personal mobility services occupies around 1%, much lower than that of other conventional transportation modes. us, it means that this study focuses on peopleโs intentions who live in cities where shared mobility is not prevailing
B is a predicted value, meaning influence of the variable and Beta is the standardized value of B; S.E is the standard error, which estimates the variability; t is a value that is the difference between the predicted value divided by standard error, which compares the differences according to variability; Sig. judges whether it is valid within the significance level (95% confidence level in this study); allowance and variance inflation factor (VIF) are indicators of multicollinearity
Summary
As negative impacts of autoownership have gradually increased, such as significant energy consumption, traffic congestion, inefficient land use, and excessive vehicle purchasing and operating costs, drivers have begun to look for alternatives [1]. Shared mobility, a service that allows users to share various transportation modes and use them when necessary [2], has emerged as a major solution to existing transportation problems [1, 3,4,5]. Personal mobility-sharing services were first introduced in Amsterdam, the Netherlands, in 1965, in a system that allowed people to share public bicycles; it was difficult to proceed with long-term planning as a result of bicycle damage or theft issues [8]. Demand for alternative mobility has been continuously increasing according to its advantages (e.g., low cost, autonomy, flexibility, and rental in recent years) [9] and the disadvantages of owning personal cars (e.g., increased urban problems and uncertainty in future operation expenses) [1]. With the successful introduction of shared mobility service in Europe thanks to advancements in telecommunication systems, many related studies have been carried out from different perspectives such as infrastructure planning for car-sharing and bicycle-sharing services [10, 11], estimation of benefits and impacts [12,13,14,15,16], social
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