Shared mobility services towards Mobility as a Service (MaaS): What, who and when?

There is a growing popularity for shared mobility services. With their penetration in a city, a natural phenomenon is the mode shift from conventional modes. Therefore, there is a need for a model, which is capable of capturing this phenomenon. While most existing studies have developed mode choice models consisting of a single shared mobility service, only a few studies of two modes exist. Nevertheless, no study has focused on the development of a joint mode choice model for bike-sharing, car-sharing and ride-hailing services. Hence, the objective of this research is to develop a mode choice model, which is capable of capturing the demand for the aforementioned three services simultaneously. The estimation results show the influence of socio-demographic characteristics (age, gender, education, household car-ownership and possession of public transport pass and license), trip-related variables (trip distance and travel time) and supply parameter (fleet size). For example, bike-sharing systems are more likely to be used for trips with distances up to 5 km, while car-sharing and ride-hailing systems are expected to be used for a longer distance range of 2 to 15 km. However, there is a lower probability to use the three services for travel times beyond 30 min. Discussions are included for the integration of the developed mode choice model into the transport simulation systems. In addition, based on the influential factors, policy measures are suggested under the following categories: (i) Finance (e.g., microsubidies), (ii) Infrastructure (e.g., dedicated cycle lanes), (iii) Campaigns and nudges (e.g., social awareness campaigns), and (iv) Service design (e.g., integration with public transport). Especially, measures that benefit women and attract young and older age groups are proposed. Besides the policy measures, the probable demand segments for the three shared mobility services have been identified and summarized, with a focus to integrate them along with public transport for Mobility as a Service (MaaS). This includes how the three services can be streamlined to target different distance categories and socio-demographic groups. The contributions from this study can allow several cities to estimate more accurately the mode shares for the shared mobility services and also to promote sustainable usage of shared mobility services through MaaS platforms.