Expected Success of Shared Mobility and Implications on Vehicle Ownership, Forecast to 2022
本報告提供北美各國的私人汽車擁有數的估計值、預測值，及MaaS (行動「即服務」) 及共享移動服務的成功預測相關分析，兩個公司的相關性和共通的影響要素，產業相關人員今後應致力處理的課題等資訊彙整，為您概述為以下內容。
Predicting Success of MaaS in North America
The Expected Success of Shared Mobility and Implications on Vehicle Ownership takes a holistic approach to determining the success of new shared mobility platforms in specific urban areas as well as the implications on personal vehicle ownership. First, the study analyzes key market drivers and restraints, interpreting factors such as declining vehicle sales, competition from new ridehailing entrants, and proliferation of shared mobility services. This in addition to statistics around urbanization, declining public transit 'ridership', and investment from major tech players and OEMs guides the analysis of key variables.
From these variables, such as total cost of vehicle ownership, access to public transportation, mobility service offerings, congestions/traffic patterns, and urban sprawl/city size, we create various scores for select geographies. The cost and convenience score, congestion score, mobility score, and public incentive score help to frame how the variables influence personal vehicle ownership. Applying these variables to the cities of Seattle, Dallas, and Detroit explains why consumers may or may not choose to relinquish a personal vehicle.
While the likelihood to own a personal vehicle strongly influences the success of shared mobility platforms, it is important to dig deeper to uncover other factors that may determine how new mobility services will fare in select geographies. With that in mind, we analyze variables such as population density, tech saturation, parking cost/availability, urban design, and access to public transportation to create scores for select geographies. This includes the entrance score, pedestrian friendliness score, accessibility score, and public incentive score to understand if mobility services are likely to thrive in the aforementioned cities.
Once scores have been calculated from both quantitative and qualitative information, analysis and interpretation of both the success of mobility services and likelihood of personal vehicle ownership specific geographies becomes understandable. By taking an average of all scores, it is possible to compare geographies on the same scale. As such, this study serves as a framework to interpret success of shared mobility services and likelihood of personal vehicle ownership across cities in the US.
This model grows as new information is gathered, demographics, services, regulation, initiatives and variables change. The flexibility provided by this study can help guide future analysis of the topics researched in various urban settings. New mobility and powertrain innovations are certain to change consumer ownership and transit habits. Urban infrastructure development will cause some cities to be more attractive for public transit ridership, personal vehicle ownership, and mobility service adoption. With this in mind, the weight assigned to various scores can be easily adapted with societal changes.