Guidelines for Analysis of Investments in Bicycle Facilities (2006) / Chapter Skim
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Appendix A: Estimating Bicycling Demand
Appendix A: Estimating Bicycling Demand
Pages 60-65
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From page 60... ...
and Texas Transportation Institute (44) completed major surveys of non-motorized modeling techniques in the late 1990s; the majority of the efforts they describe focused on predicting either commute shares or total bicycle travel by reference to these types of basic factors.
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This survey was done over an entire year, which makes it possible to measure seasonal variations. Both of these surveys involved households keeping travel diaries on a randomly assigned day; these days were spread throughout the week, and throughout the year for each geographic area.
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A-3 MODELING BICYCLING DEMAND Traditional approaches to modeling bicycle demand are derived from the standard methods used for forecasting auto travel. That is, they start from basic information about the people and the transportation environment in an area and use this in some way to predict an amount of bicycle travel, either directly, or as the solution to a mode choice problem in a larger travel model.
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From page 63... ...
Ordinary measures of goodness of fit have little meaning in this sort of environment; we focus instead on more heuristic measures such as the number of observations that fit within the predicted confidence interval. Metropolitan Statistical Areas Combining census data with our NHTS analysis produced 15 MSAs for which we had both commute to work shares by bike and total percent of adults biking on their survey day.
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This equation can be used to generate a predicted total riding share for each city. Given this predicted share and the NHTS sample size, a 95% confidence interval of expected number of adult bicyclists in the sample can be calculated assuming a binomial function.
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as a whole. Figure 8 shows lines representing rough boundaries on the observed values for daily cyclists, as they relate to bicycle commute shares at the MSA and state level.
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