Making Do with Less: Calibrating a True Travel Demand Model Without Traditional Survey Data
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Making Do with Less: Calibrating a True Travel Demand Model Without Traditional Survey Data

  • Published Date:

    1997

  • Language:
    English
Filetype[PDF-216.11 KB]


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  • TRIS Online Accession Number:
    789769
  • NTL Classification:
    NTL-PLANNING AND POLICY-PLANNING AND POLICY ; NTL-PLANNING AND POLICY-Surveys ; NTL-PLANNING AND POLICY-Travel Demand ; NTL-REFERENCES AND DIRECTORIES-Statistics ;
  • Abstract:
    For many small and medium-sized cities, funding a full Home-Interview survey, with costs as high as $100 per household, is not feasible. Traditionally, such a survey has provided the basic foundation for developing a truly useful travel demand model. Without it, model development has been handicapped by the lack of such behavioral, disaggregate data. Using a set of consistent, concurrently taken counts, external travel behavior from an older study, and a detailed zone system, a technique has been developed that can produce a fully specified, classical travel demand model. The technique relied on the fact that a sufficiently robust set of simple traffic counts contain a great deal of travel behavior information implicitly. Not only can this model be calibrated quite closely to existing counts, but it can be used as a forecast tool, requiring only socioeconomic and network data. In other words, the traffic count data was tied not only to a current origin-destination (O-D) trip table, but also to distribution parameters, time of day parameters, and trip generation rates at the zone level. The procedure involves an extended application of the O-D from traffic count technique (which is implemented through a macro in EMME/2), essentially working the four-step modeling process in reverse. Through iterations of the model, proper generation and distribution parameters can be developed, which, when finished, reliably reflect actual conditions. This technique can be a very cost-effective way for small and medium-sized cities to obtain a travel demand modeling capability which is not simply a product of borrowed parameters from another area, but is indeed calibrated to local, observed conditions. This paper describes this technique, and the results of an application in Jamestown, ND. 10p.
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