Using GPS and Accelerometers to Identify Preferred Locations for Physical Activity Participation and Commuter Mode Choice

Abstract

As cities seek to foster more livable and active communities, much attention is being placed on promoting physical activity through active transport and wellness activities. As such, it is important that city planners and engineers be able to tailor these livable improvements to the interests and needs of their specific residents. More importantly, it is important to understand where individuals are most likely to participate in physical activities as well as their level of interest in pursuing these activities. This thesis develops a unique GPS and accelerometer-based methodology for collecting and analyzing (through a series of regression models) university students’ levels of interest in physical activities that take place at/near home, at a destination or during transportation. It also utilizes a discrete choice model to determine the factors influencing students’ commute mode choices. As a result, it was possible to determine where students were most physically active through observed activity data. Both built environment and health/lifestyle variables significantly influenced physical activity as well as mode choice. These methods and the models estimated in this paper can be applied on a larger scale to communities to forecast locations of physical activity participation for use in guiding the development of livable communities.