Improving ADA Complementary Paratransit Demand Estimation (2007) / Chapter Skim
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The modeling process requires a choice of mathematical forms to be used in the regression procedure. This section describes the advantages and disadvantages of several mathematical forms, leading to a recommendation for the most promising ones.
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Exhibit 4-1. ADA trips and service area population (linear)
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Exhibit 4-2. ADA trips and service area population (logarithmic)
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Model Development 21 Some variables, such as fare, do not need to be put in per-capita form. Models that predict ADA trips per capita would probably have no population term, for example, (purely for illustration)
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service area. However, it produces the same impact regardless of the initial ridership level.
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Model Development 23 Logarithmic Model of Total ADA Paratransit Trips The preliminary data analysis identified a number of possible problems with variables, including some variables that clearly cannot be present together in a model. However, only a handful of variables were initially excluded from consideration: • Separate age variables by sex were not used because they are so highly correlated.
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percent found conditionally eligible = 0%, conditional trip screening is not used, and effective window = 1 minute. This has no practical meaning.
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Exhibit 4-7. Logarithmic model of total ADA paratransit trips with revenue vehicle miles.
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higher levels of fixed-route transit service correspond to higher levels of paratransit trip making. Further transformation of RVM -- e.g., RVM per square mile per capita -- did not produce significant results.
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Exhibit 4-10. Observed and predicted trips per capita: Regression 1.
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Exhibit 4-11. Observed and predicted trips per capita: Regression 2.
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Exhibit 4-12. Model of trips per capita.
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30 Improving ADA Complementary Paratransit Demand Estimation 0 200 400 600 800 1000 1200 1400 1600 1800 2000 O TA BT Li nk JA UN T W TA BF T EC CT A LT D CA TA M VR TA Tu ls a CC CT A CN YR TA FA X FW TA H AR T SO RT A SM CT D R IP TA TR IM ET PA AC Ki ng UT A SC VT A R TD D AR T O CT A N YC A D A T rip s in th ou sa nd s Observed ADA Trips Predicted ADA Trips Exhibit 4-13. Observed and predicted trips: Regression 3.
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position of the regression line at the point where the average values for all the explanatory terms are used.5 As for the total trip model, RVM per capita was nearly significant. Again, because of its implications for further research, the regression with this variable included is shown in Exhibit 4-15.
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The effect of eliminating certain systems from the regression was also tested. Since New York City is so different from all other systems, the regression was repeated with its data removed.
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coefficient of −0.264 is consistent with a negative effect of hold times on demand. However, the estimated value of Student's t, −1.626, corresponds to a probability of 0.119 that the estimated coefficient is due to chance (i.e., a "confidence level" of only 88%)
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