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Estimating current modal splits.
  • Published Date:
    2005-11-01
  • Language:
    English
Filetype[PDF-308.82 KB]


Details:
  • Resource Type:
  • Geographical Coverage:
  • NTL Classification:
    NTL-PLANNING AND POLICY-PLANNING AND POLICY
  • Format:
  • Abstract:
    This project is the second part in a two part modeling effort. In previous work*, mode

    choice was modeled by examining characteristics of individuals and the trips they make.

    A study of the choices of individuals is necessary for a fundamental understanding of

    travel mode choice. Models were built to estimate mode split at the State and County

    level. Where transit or walk trips often account for only 1 to 5% of all trips, the main

    problem in modeling the use of other choices of travel besides the personal auto is that

    there is very little data available. The modeling difficulty becomes greater as estimates

    of mode split are desired for smaller levels of geography, such as for a traffic zone, rather

    than a County.

    For use in travel demand forecasting and examination of transit markets, almost all mode

    choice models used by transportation agencies are developed using aggregate level data,

    typically at the level of a traffic zone, such as population totals, mean incomes, average

    household characteristics, and other summary data. The reason for this is that aggregate

    data, such as provided by the U.S. Census, is typically more available. For the most part,

    estimates of travel mode split used in travel demand models are not very sophisticated

    and often consist of an estimate based on fixed percentage of trips (e.g. 1% of trips in a

    zone will be accomplished by using transit) rather than a model considering a number of

    factors.

    This project investigates how travel mode split can be modeled using aggregate data at

    smaller levels of geography like traffic zones for use in route planning and travel demand

    forecasting. It starts with models based on individual data developed at the county level

    and investigates the applicability of these models at smaller levels of geography where

    aggregate estimates of the factors are available.

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