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14 Zonal population and Network supply data Synthetic population land-use data by time of day Full-Day Activity Pattern Predicted tours by purpose Accessibility logsum values by and chain type tour purpose and tour type Home-Based Tour Times of Day Accessibility logsum values by Predicted tours by purpose, chain tour purpose, tour type, and type, and time of day time of day Home-Based Tour Mode and Destination USER'S GUIDE Accessibility logsum values by Predicted tours by purpose, chain Work-Based Subtour tour purpose, tour type, time of type, time of day, and mode Models day, mode, and destination (not used in current version of model) Location of Intermediate Stops (car driver tours only) Output: OD trip matrices by mode, purpose, time of day, and income class Figure 3. Information flows in the Portland Tour-Based Model. during the peak periods, and the research team reason- 4.4 ALTERNATE METHODS ably ignores off-peak mode shifts. FOR DERIVING ELASTICITIES Most users of the NCHRP 25-21 methodology can prob- 4.3 ELASTICITIES ably use the elasticities provided in Table 6 without having to repeat the application of the Portland model to the Seat- The final set of elasticities fitted to the Portland Tour- tle test bed. However, tour-based models like Portland are a Based Model is shown in Table 6. As shown in the table, a recent development. Little is known about the robustness of 10-percent decrease in AM peak-period travel time for drive their parameters when applied to other areas. Consequently, alone would result in the following predicted demand effects: researchers cannot state with assurance that a particular tour- based model can be applied to similar or dissimilar urban A 2.25-percent increase in drive alone during the regions. AM peak, Analysts with greater resources can apply the Portland A 0.37-percent decrease in shared ride during the model or another tour-based model to their own urban region AM peak, as described in the above sections to see how elasticities A 0.36-percent decrease in transit riders during the derived from application of the tour-based model to their own AM peak, region vary from those shown in Table 6. Locally derived A 1.24-percent increase in drive alone during the PM elasticities would presumably be more reliable than ones bor- peak, and rowed from another region, but, again, there is little or no A 1.70-percent increase in drive alone during the off peak. practical experience to back up this conjecture.

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15 TABLE 6 Travel time elasticities Demand Travel Time AM peak PM peak DA SR TR DA SR TR AM peak DA -0.225 0.030 0.010 -0.024 0.000 0.000 SR 0.037 -0.303 0.032 0.000 -0.028 0.000 TR 0.036 0.030 -0.129 0.000 0.000 -0.007 PM peak DA -0.124 0.000 0.000 -0.151 0.015 0.005 SR 0.000 -0.109 0.000 0.019 -0.166 0.016 TR 0.000 0.000 -0.051 0.018 0.015 -0.040 Off peak DA -0.170 0.000 0.000 -0.069 0.000 0.000 SR 0.000 -0.189 0.000 0.000 -0.082 0.000 TR 0.000 0.000 -0.074 0.000 0.000 -0.014 Note: DA = drive alone, SR = shared ride, TR = transit. Source: Portland Tour-Based Model Applied to PSRC data set. Estimates (shown in italics) appear in the table when statistically significant results could USER'S GUIDE not be estimated from the data set. Zero values are shown for cross-elasticities that were deemed (a priori) to be insignificant.