Active mobility , traditionally referring to modes requiring physical activity to operate, offers an array of physical, emotional, and social well-being benefits. However, with the proliferation of the sharing economy, new nonmotorized means of transport are entering the fold, complementing some existing mobility options while competing with others. The purpose of this research  is to investigate the adoption of three active travel modes , namely walking, cycling and bike-sharing , in a joint modeling framework. The analysis is based on an adaptation of the stages of change framework, which originates from the health behavior sciences. The development of a multivariate ordered probit model drawing on U.S. survey data provides well-needed insights into individuals preparedness to adopt multiple active modes as a function of personal, neighborhood and psychosocial factors. The joint model structures reveal different levels of interdependence among active mobility choices. The strongest positive association is found for walking and cycling adoption , whereas other joint model effects are less evident. Identifying strongly with active mobility  , experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions. This study contributes to the understanding of how decisions within the same functional domain are related  .
Active mobility offers an array of physical, emotional, and social wellbeing benefits. However, with the proliferation of the sharing economy, new nonmotorized means of transport are entering the fold, complementing some existing mobility options while competing with others. The purpose of this research study is to investigate the adoption of three active travel modes ; namely walking, cycling and bikesharing , in a joint modeling framework. The analysis is based on an adaptation of the stages of change framework, which originates from the health behavior sciences. Multivariate ordered probit modeling drawing on U.S. survey data provides well-needed insights into individuals preparedness to adopt multiple active modes as a function of personal, neighborhood and psychosocial factors. The research suggests three important findings. 1) The joint model structure confirms interdependence among different active mobility choices. The strongest complementarity is found for walking and cycling adoption . 2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. 3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying strongly with active mobility aspirations , experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions. This study contributes to the understanding of how decisions within the same functional domain are related and help to design policies that promote active mobility by identifying positive spillovers and joint determinants .
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, traditionally referring to modes requiring physical activity to operate,
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well-being
wellbeing
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study
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,
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bike-sharing
bikesharing
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The development of a multivariate ordered probit model
Multivariate ordered probit modeling
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joint model structures reveal different levels of interdependence among
research suggests three important findings. 1) The joint model structure confirms interdependence among different
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positive association
complementarity
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, whereas other joint model effects are less evident. Identifying
. 2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. 3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying
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aspirations
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and help to design policies that promote active mobility by identifying positive spillovers and joint determinants