Expected Success of Shared Mobility and Implications on Vehicle Ownership, Forecast to 2022
発行: Frost & Sullivan
ページ情報: 英文 52 Pages
当レポートでは、北米諸国のマイカー保有台数の推定値・予測値や、MaaS (「サービスとしての」モビリティ) およびシェアードモビリティサービスの成功見通しについて分析するとともに、両社の関連性や共通する影響要因、業界関係者が今後取り組むべき課題、といった情報を取りまとめてお届けいたします。
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.