Modeling the Spatial and Temporal Dimensions of Recreational Activity Participation with a Focus on Physical Activities
MetadataShow full item record
This study presents a unified framework to understand the weekday recreational activity participation time-use of adults, with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis is important for a better understanding of how individuals incorporate physical activity into their daily activities on a typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore, such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling, since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation. The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how much’ individuals choose to participate in ‘what type of (recreational) activity’.
At the time of publication I.N. Sener was at Texas Transportation Institute and C.R. Bhat was at the University of Texas at Austin.
CitationSener, I.N., and C.R. Bhat (2012), "Modeling the Spatial and Temporal Dimensions of Recreational Activity Participation with a Focus on Physical Activities," Transportation, Vol. 39, No. 3, pp. 627-656.
Showing items related by title, author, creator and subject.
Kong, Danxia (2011-12)Increasingly, predictive models are used to support routine business de- cisions and are integral to the strategic competitive business strategies for a wide range of industries. Most often, data-driven predictive models ...
Rajagopalan, Bharath S.; Pinjari, Abdul R.; Bhat, Chandra R. (National Academy of Sciences, 2009)This study contributes to the literature on activity time-use and activity timing analysis by developing a comprehensive, high resolution, out-of-home non-work activity generation model that considers daily activity time-use ...
An Analysis of Children's Leisure Activity Engagement: Examining the Day of Week, Location, Physical Activity Level, and Fixity Dimensions Sener, Ipek N.; Copperman, Rachel B.; Pendyala, Ram M.; Bhat, Chandra R. (Springer, 2008)This paper presents a detailed analysis of discretionary leisure activity engagement by children. Children's leisure activity engagement is of much interest to transportation professionals from an activity-based travel ...