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Abstract:Methodology to efficiently segment markets for public transportation offerings has been introduced and exemplified in an
application to an urban travel corridor in which high tech companies predominate. The principal objective has been to introduce
and apply multivariate methodology to efficiently identify segments of work commuters and their demographic identifiers. A set
of attributes in terms of which service offerings could be defined was derived from background studies and focus groups of
work commuters in the county. Adaptive choice conjoint analysis was used to derive the importance weights of these attributes
in available service offering to these commuters. A two-stage clustering procedure was then used to explore the grouping of
individual’s subsets into homogeneous sub-groups of the sample. These subsets are commonly a basis for differentiation in
service offerings that can increase total ridership in public transportation while approximating cost neutrality in service delivery.
Recursive partitioning identified interactions between demographic predictors that significantly contributed to the discrimination
of segments in demographics. Implementation of the results is discussed.
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