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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>TRIP GENERATION MODELING USING CHAID, A CRITERION-BASED SEGMENTATION MODELING TOOL</title>
      <link>https://trid.trb.org/View/541943</link>
      <description><![CDATA[Conventional trip generation models are identified, as are the difficulties of model application typical of segmentation problems:  identification and categorization of explanatory variables and of the interactions among them.  The use of CHAID (Chi-Squared Automatic Interaction Detection), a criterion-based segmentation modeling tool, is explored to analyze household trip generation rates.  CHAID models are presented in the form of a tree, each final node representing a group of homogenous households concerning daily trip making.  An application to data from an origin-destination survey for Sao Paulo produced interesting results, in agreement with theoretical expectations and amenable to interpretation based on the likely activity-travel patterns of each group of households generated by the technique.  CHAID can be used as an exploratory technique for aiding model development or as a model by itself.  The use of CHAID results as a trip generation model was verified through an evaluation of its predictive capability in a cross comparison of two subsamples and through a comparison of observed versus predicted trips at a zone level; the segmentation of households produced by the technique provided good estimates of trip rates and zone totals.  The application of a modeling approach requiring a highly disaggregate projection of the population may become possible considering the advances in methods for the generation of synthetic populations.  The use of these methods in conjunction with a segmentation model represents an alternative to conventional trip generation models and an opportunity to introduce new population forecasting techniques into transportation planning practice.]]></description>
      <pubDate>Fri, 11 Dec 1998 00:00:00 GMT</pubDate>
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