Geography Reference
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Crane (
1996
, 2001) explicitly incorporates the built environment into the afore-
mentioned travel demand theory based on the utility maximization principle of
microeconomics (Boarnet and Crane
2001
;Crane
1996
). It has been reasoned that
when land use variables exert an influence on travel behavior, such an influence is
seen in the effect on the relative trip cost (e.g. speed and distance) of the available
modes. To include the built environment in an explicitly activity-based framework,
Matt and his colleagues illustrates how the built environment can influence trade-
offs between utility and cost (Maat et al.
2005
). While compact built environments
may reduce an individual's travel time and ability to obtain the same amount of
activity benefits, the timesaving benefits of compact designs may also increase
trip generations. Whether saving travel time results in less travel, longer trips to
obtain extra utility or allocation of time to other activities remains in question. Maat
points out that an individual's aim is not primarily to minimize travel costs, but to
maximize utility within space and time constraints.
Walking for leisure can also gain theoretical support from activity-pattern
studies. A better understanding of how individuals incorporate leisure walking into
their daily activities allows for the development of effective policy interventions
to facilitate more walking. Furthermore, because recreational activities comprise a
substantial share of individuals' non-work activities, studies of participation and
time use in recreational activity episodes contribute to activity-based travel demand
modeling. There has been relatively little attention paid to the spatial and temporal
contexts of physical activity participation, that is, on the when, where and how
long of physical activity participation (Sener and Bhat
2012
). Leisure-walking
participation studies can provide important insight into the design of customized
physically active lifestyle promotion strategies in different built-environment and
time-of-day contexts.
5.3.2
Forecast-Oriented Behavior Theory
Travel modal choices form the skeleton of activity patterns. Choices in walking,
transit or driving are discrete by nature. Discrete choice models are thus very helpful
for this kind of analysis, which is based on the assumption that choice alternatives
can be represented as bundles of attributes. Individuals are assumed to derive some
utility from these attribute values and combine them into an overall measure of
utility (Ben-Akiva and Lerman
1985
). Because of measurement errors and taste
variations, these utilities are assumed to comprise a systemic measurable component
and a random term. Depending on the assumptions made in these error terms, choice
probabilities can be derived.
However, the forecast-oriented travel behavior theory tends to limit the variables
included in models. Rather than the larger set of variables, only factors that can be
forecasted and that researchers believe might affect travel behavior are employed.
McFadden recognizes the importance of the perceptions and attitudes of individuals,
but argues that such factors cannot be forecasted and hence should be excluded
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