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Household travel surveys: Where are we going?

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  • Stopher, Peter R.
  • Greaves, Stephen P.

Abstract

In this paper, we commence by reviewing the recent history of household travel surveys. We note some of the problems that contemporary surveys are encountering throughout the world. We also review the data demands of current and emerging travel demand models, concluding that there are many new demands being placed on data, both in terms of the extent of the data required and the accuracy and completeness of the data. Noting that the standard method for conducting most household travel surveys is, and has been for some years, a diary, we briefly explore the evolution of the diary survey from the late 1970s to the present. In the next section of the paper, we explore a number of facets of potential future data collection. We include in this the use of GPS devices to measure travel, the potential of panel designs and some of the alternatives within panel designs, the development of continuous household travel surveys, especially in Australia, and the emerging capabilities in data fusion. Using some of these emerging methods for data collection and data simulation, we then propose a new paradigm for data collection that places the emphasis on a paid, national panel that is designed as a rotating, split panel, with the cross-sectional component conducted as a continuing survey. The basis of the panel data collection is proposed as GPS with demographic data, and the continuing national sample would also use GPS at its core. The potential to add in such specialised surveys as stated choice and process surveys is also noted as an advantage of the panel approach. We also explore briefly the notion that a special access panel or panels could be included as part of the design.

Suggested Citation

  • Stopher, Peter R. & Greaves, Stephen P., 2007. "Household travel surveys: Where are we going?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 367-381, June.
    • Handle: RePEc:eee:transa:v:41:y:2007:i:5:p:367-381
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    References listed on IDEAS

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    1. Jan Veldhuisen & Harry Timmermans & Loek Kapoen, 2000. "RAMBLAS: A Regional Planning Model Based on the Microsimulation of Daily Activity Travel Patterns," Environment and Planning A, , vol. 32(3), pages 427-443, March.
    2. Sean Doherty & Eric Miller, 2000. "A computerized household activity scheduling survey," Transportation, Springer, vol. 27(1), pages 75-97, February.
    3. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
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