Sample Size Implications of Multi-Day GPS-Enabled Household Travel Surveys (2016) / Chapter Skim
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NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM October 2016 Responsible Senior Program Officer: Lawrence D Goldstein C O N T E N T S Summary, 1 Chapter 1 Introduction, 2 Chapter 2 Data and Method, 7 Chapter 3 Bias Checks, 13 Chapter 4 Results for Tabular Data Summaries, 15 Chapter 5 Results for Model Estimation, 19 Chapter 6 Summary of Results, 24 Chapter 7 Conclusions and Recommendations, 26 References, 31 Appendixes, 32 Research Results Digest 400 SAMPLE SIZE IMPLICATIONS OF MULTI-DAY GPS-ENABLED HOUSEHOLD TRAVEL SURVEYS This digest summarizes key findings of research conducted in NCHRP Project 08-36/Task 123, "Survey Sample Size and Weighting." This digest is based on the project final report by Louis Rizzo of Westat and Gregory D
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2incorrect. This research dem onstrated how the jackknife can be used to estimate the true variance and overcome these issues.
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3maintaining diaries or recalling travel. Multiday travel surveys are now more feasible, given Global Positioning System (GPS)
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4were less likely to report trips as they became more fatigued. In addition, the Chicago survey collected part of the sample using a 1-day travel diary, allowing for a comparison of the 1-day and 2-day diaries.
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5team used the jackknife to provide an unbiased estimate of the true variance. 1.1.6 Proposed Formula for Sample Size Equivalency of Multi-Day Surveys Parsons Brinckerhoff et al.
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6to 1. This research measures the design effects using a real-world survey, leaving the cost calculations to others.
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7the research tested for biases between the GPS-only sample and a GPS-with-prompted-recall sample. This research acknowledges that the repeated measurement problem may bias the variance estimates when multi-day survey data is used for model development.
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8These travel choices were selected to illustrate the effect of multi-day data on model components expected to have different characteristics and are not necessarily an argument for or against any particular model design. For each travel choice, the basic unit of analysis was the unit that would be modeled in a travel demand model.
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9The GPS-with-prompted-recall data was only fully processed for Day 1 as part of the original survey work. This was not an inherent limitation of the method, but the focus of the 2012 Northeast Ohio Regional Travel Survey was on getting a complete Day 1, and the later days were not a priority in terms of resources.
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10 If both samples provided an equal measure of the underlying travel behavior, the means of the two samples (for the selected travel characteristics) should be the same.
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11 estimation in the 2009 National Household Travel Survey [see 2009 NHTS User's Guide (2011)
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12 In terms of design effects, y_(2) has twice as many days, so one would expect the variance to be half as much if each extra day per household is providing as much new information as the first day.
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13 mation (e.g., trip purpose and mode) by asking the respondents, the equivalent information was imputed for the GPS-only observations.
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14 bias checks from picking up this effect, at least in the full data set. The research team compared the following travel characteristics across collection days: • Mean trips per person • Mean trips per person by trip purpose • Mean trip length and trip duration by persontrip (and also by trip-purpose domain)
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15 4.1 Automobiles Owned by County The number of automobiles owned for each household did not differ across day at all -- this question was only asked in the household interview. The estimates for totals and percentages are given in Appendix E-1.
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16 4.3 Average Tours by Tour purpose (by person Type) The third type of analysis is of the jackknife standard errors for average number of tours per person per day.
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17 4.4 County-to-County Trip percentages The fourth type of analysis is of the jackknife standard errors for percentages of trips by start-county/ end-county pair, based on the three sets of files (i.e., 1-day, 2-day, and full)
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18 here. Table 4-6 shows the number of observed trips by mode, for the GPS-with-recall segment and each day of the GPS-only segment.
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19 CHApTER 5 RESulTS fOR MODEl ESTIMATION Chapter 2 introduced the framework used to measure the effect of using the 1-, 2-, or 3-day sample in a statistically robust way. This was done for the case where the measures of interest were mean values from a survey, as might be used to summarize travel in a region or as targets against which to calibrate a travel model.
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20 about their automobile ownership. The results of the model estimation are given in Appendix G-1.
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21 as added information. The rates of homogeneity are higher for workers, which is expected, but the difference is not that great.
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22 the 1-day file (the benchmark) , the 2-day file, and the full file.
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23 1-day file (the benchmark) , the 2-day file, and the full file.
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24 dixes E and G An average a factor was computed for each of the tabular summaries referenced and each of the model estimation summaries referenced.
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25 (such as from the Census Transportation Planning Package)
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26 The bias checks reveal that it is important to avoid bias in the later collection days, and it appears that this will require commitment of resources to make sure there is no fall-off between the first collected travel day and later collected travel days. So it appears that the ratio q/p should not become too small (i.e., the later collection days should not be too inexpensive)
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27 lar estimates when the extra (i.e., third) travel day was included.
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28 the GPS-only approach needs further development before it can be relied on as a stand-alone paradigm. The GPS-with-recall-interview approach is certainly a much more reliable way of collecting much more data about the travel behavior, but the necessity of a special extra recall interview is burdensome and limits the number of collection days that can be supplemented with a recall interview.
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29 should be subject to similar bias checks before they are used for travel model development. Assuming the data pass the bias checks, the key factor to consider when using multi-day survey data is the repeated measurement problem, as discussed in Section 1.1.5.
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30 a range of cost factors. This research does not provide new evidence in relation to the cost factors, but only reports them based on a limited number that have appeared in the literature.
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31 REfERENCES Note: Includes references both for main text and Appendix A 2009 National Household Travel Survey User's Guide (2011)
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32 Stopher, P
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Transportation Research Board 500 Fifth Street, NW Washington, DC 20001 These digests are issued in order to increase awareness of research results emanating from projects in the Cooperative Research Programs (CRP)
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