MARKET SEGMENTATION AS A MECHANISM IN ALLOWING FOR VAIBILITY OF TRAVELLER BEHAVIOUR

A major problem with aggreate transport planning models is the accounting of variability in traveller behaviour when the basic unit of analysis is the geographical traffic zone. In an attempt to allow for this variance, recent attention has been given to the role of socio-economic (user and household) characteristics in systematically identifying a homogeneous grouping of travellers with respect to the issue under study rather than restricting the grouping definition according to physical geographical criteria alone. This homogeneous grouping criterion combined eventually with a necessity to represent travel demand in a spatial context, can assist in improving our ability to explain real travel patterns by the development of an improved aggregation condition. The emphasis is on modelling homogeneous groups of travellers separately, and then relating the individual sets of results to each other to obtain an aggregate prediction of behaviour via a knowledge of the representativeness of each group contained in the total sample. This paper presents a technique to identify the relative homogeneity of travellers in accordance with a specified criterion, and illustrates its use with individual household data for the Sydney Metropolitan Area. The paper concludes with a dscussion of the advantages of segmentation in operational transport planning, in particular with reference to the "aggregation" of disaggregate behavioural travel choice models, or movement from a micro-model of individual choice behaviour to an aggregate model of travel demand. /Author/

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  • Corporate Authors:

    Elsevier

    Radarweg 29
    Amsterdam,   Netherlands  1043 NX
  • Authors:
    • Hensher, D A
  • Publication Date: 1976-9

Media Info

  • Features: Figures; References; Tables;
  • Pagination: p. 257-284
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00141870
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 16 1977 12:00AM