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01.05.2012

Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities

verfasst von: Ipek N. Sener, Chandra R. Bhat

Erschienen in: Transportation | Ausgabe 3/2012

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Abstract

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’.

Vorheriger Artikel A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study

Nächster Artikel Flexible spatial dependence structures for unordered multinomial choice models: formulation and application to teenagers’ activity participation

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The current adult physical activity guidelines call for at least 150 min a week of moderate-level physical activity (such as brisk walking, bicycling, water aerobics) or 75 min a week of vigorous-level physical activity (such as jogging, running, mountain climbing, bicycling uphill) (USDHHS 2008).

The current guidelines call for children and adolescents to participate in at least 60 min of physical activity every day, and this activity should be at a vigorous level at least 3 days a week (USDHHS 2008).

The selection of the four time periods is primarily based on reflecting work-related constraints that may make it difficult for employed individuals to participate in recreation during certain times of the day. Further, our partitioning provides information on activity participation behavior during the AM and PM peak travel periods, which is useful from a travel modeling perspective. Of course, other time periods could also be used, but our partitioning seems also a rather natural one from a scheduling perspective. A descriptive analysis of all physically active recreational episodes showed that 76% of episodes beginning within any of these periods also ended within the same period. For the remaining 24% of episodes that straddle periods (40% of which had a duration of over 3 h), the assignment of the episode’s duration is split between time periods based on the duration of the episode in each time period.

The particular emphasis on physically active recreation in this paper is because of the obvious confluence of interest in this kind of recreation from both a public health perspective as well as a transportation perspective.

As indicated above, the MDCEV model is based on a non-linear utility structure for each alternative. In such a structure, or any other utility-theoretic satiation-based structure, it becomes difficult to estimate the non-linear shapes of the utility functions when one alternative consistently (and across all individuals) “hogs” up a very large amount of time relative to other alternatives. This is the reason why we do not define the time allocated to the “non-recreational” activity category as 24 h minus the time spent on recreational pursuits (this would lead to very high durations for the “non-recreational” activity category). Rather, we remove out work, work-related, and sleep time durations from 24 h, and use the resulting duration as the time available for participation in recreational pursuits, which then forms the basis for determining the time allocated to the “non-recreational” activity category, as discussed earlier.

The term “outside good” refers to a good that is “outside” the purview of the choice of whether to be consumed or not. That is, the “outside good” is a good that is always consumed by all consumers.

Several other additive, non-linear, utility forms, as proposed by Bhat (2008), were also considered. However, the one provided below was the best form (that is, provided by far the best data fit) in the empirical analysis of the current paper.

As discussed in "The current paper", the total time available for recreational activities (T) is computed as 24 h minus the time invested in sleep, work, and work-related activities.

A physically active episode requires regular bodily movement during the episode, while a physically passive episode involves maintaining a sedentary and stable position for the duration of the episode. For example, swimming or walking around the neighborhood would be a physically active episode, while going to a movie is a physically inactive episode.

Note, however, that individuals who drop off/pick up others from the pool will report their activity type as “pick-up/drop-off” and so this episode will not be considered as a physically active one, Also, there is some possibility that individuals who go to a pool and not swim will report their activity type as “social” or “resting/relaxing”, in which case these episodes will also not be characterized as “physically active” in our taxonomy.

Due to privacy considerations, the point coordinates of each household’s residence are not available; only the TAZ of residence of each household is available. The average size of a TAZ is 9.7 square miles.

The sum of the entries for the number of individuals participating in PAR alternatives across activity locations is greater than 1512 (the total number of individuals participating in PAR) because some individuals may participate in PAR at multiple locations in the same day. The same is true for the number of individuals participating in PAR alternatives across times-of-day because some individuals may participate in PAR during multiple time periods of the weekday.

To conserve on space, we do not present the baseline preference constants in Table 3. These constants do not have any substantive interpretations, but absorb the impacts of the different lengths of the time-of-day periods.

Several different threshold values were attempted to capture non-linear age-related effects in our dummy variable specification, but the thresholds of 30 and 49 years provided the best fit. In addition, this dummy variable specification was better than a continuous age specification and a specification that considered non-linear spline effects.

Of course, these seasonality findings are based on the San Francisco region. While the results are likely to be transferable to a good part of the rest of the U.S., they may not be transferable to some parts of the U.S. that, for example, may get very hot and sticky during the summer with milder temperatures during the Spring/Fall seasons.

However, the result that many built environment variables did not turn out to be statistically significant may also be a manifestation of the use of the TAZ as the spatial unit of resolution for computing transportation system/built environment variables. Future studies should consider more micro-scale measures to represent neighborhood physical environment variable effects, which would require some kind of geo-coded information on household residences.

However, we should note that, when only a constant was included in the specification of ω _k for each k, the resulting imputed values of α _k were all highly statistically significantly less than the value of 1, indicating clear evidence of satiation effects and support for the use of the MDCEV model. In this constants-only specification in satiation, the satiation parameters were generally consistent with the sample statistics in Table 1. For instance, the results showed that PAR pursued in/around residential neighborhoods have higher satiation levels compared to PAR pursued at clubs and outdoor parks/recreational areas.

We did not find any statistically significant satiation variations (for the non-recreation (NR) and in-home recreation (IHR) categories, because the variations in the durations of time investments in these categories were small relative to the overall high magnitude of durations in the categories.

These fit values should not be perceived as being low, because these are for a multiple discrete–continuous choice model, with all combinations of choices from the 15 discrete alternatives possible for an individual. In the current empirical context, each individual has a total of 2¹⁵ − 1 = 32,767 combinations to choose from. And then there is also the continuous component for each of the combinations.

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Titel: Modeling the spatial and temporal dimensions of recreational activity participation with a focus on physical activities
verfasst von: Ipek N. Sener
Chandra R. Bhat
Publikationsdatum: 01.05.2012
Verlag: Springer US
Erschienen in: Transportation / Ausgabe 3/2012
Print ISSN: 0049-4488
Elektronische ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-011-9356-7

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