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146 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 cating a stronger negative effect on travel decisions for time choice when undertaking trips that may generate WH trips during the congested periods. either very short or very long delays. Shift variables--Two kinds of shift variables are computed, namely, shift early (SE) and shift later (SL), The model statistics demonstrate that the rho-squared which measure the difference between the time period with respect to 0 is reasonable (0.191 for WH and 0.188 indicator (on a scale from 1 through 24 with 0.5 incre- for HW), but the rho-squared with respect to the con- ments) and the midpoint of the first three time periods stants (0.003 for WH and 0.014 HW) shows that the con- (a.m., midday, and p.m. peak periods). SE is used when stants account for nearly all the variation in time-of-day the time period indicator is less than the midpoint choices. While it may be desirable for the variables in the whereas SL is used when it is greater. The square of these models to account for more of the time-of-day choices, the variables is also used in the models to see the impact of primary objective of the model is to provide sensitivity to very short and very long delays on temporal choice trip characteristics, which is achieved by these models. behavior. During model estimation, these shift variables Additional steps are carried out for the time-of-day are multiplied by the delay variable as well as other vari- models: ables to see the combined effect on time-of-day choice. The coefficients for the delay variables multiplied by SE The models were estimated using the full set of and SL are significant and positive, while these are nega- variables listed above, with additional testing for the best tive when multiplied by the square of SE and SL. This specification of the shift variables. The estimation results indicates that travelers are more likely to switch their by trip purpose are shown in Tables 2, 3, and 4. TABLE 2 Home-Based Work Time-of-Day Choice Model Home to Work Work to Home Observations 6,931 6,076 Final log L 19,500.8 17,032.7 Rho-sq. (0) 0.188 0.191 Rho-sq. (constant) 0.014 0.003 Alternatives Variable Definition Coefficient tStat Coefficient tStat AM1AM10 AM Delay max(0, AM GC NI GC) 0.06172 4.7 0.4277 3.2 MD1MD10 MD Delay max(0, MD GC NI GC) 0.2834 6.0 0.3935 9.9 PM1PM10 PM Delay max(0, PM GC NI GC) 0.1747 4.2 0.100 constant EV EV Delay max(0, EV GC NI GC) 0.1714 5.7 AM1AM5 AM Shift Early AM Delay x (7.5t) 0.1121 7.7 AM1AM5 AM Shift Early2 AM Delay x (7.5t)2 0.01914 3.4 AM6AM10 AM Shift Later AM Delay x (t7.5) 0.01842 2.3 AM6AM10 AM Shift Later2 AM Delay x (t7.5)2 MD1MD5 MD Shift Early MD Delay x (12.5t) 0.1063 4.4 MD1MD5 MD Shift Later MD Delay x (t12.5) 0.09548 2.8 0.1144 4.6 PM1PM5 PM Shift Early PM Delay x (17.0t) 0.0766 2.8 0.09523 7.0 PM1PM5 PM Shift Early2 PM Delay x (17.0t)2 0 0.03593 4.8 PM6PM10 PM Shift Later PM Delay x (t17.0) 0.05933 1.8 0.1056 9.4 PM6PM10 PM Shift Later2 PM Delay x (t17.0)2 0 0.03027 6.0 AM1AM10 AM HH size min(HH size,4) 0.3419 7.6 AM1AM10 AM Low Income HH income <$45K 0.5176 5.9 AM1AM10 AM High Income HH income >$75K 0.515 4.3 AM1AM10 AM Crossing dummy(Bridge_N > 0) 0.3545 2.3 MD1MD10 MD HH size min(HH size,4) 0.3427 6.6 MD1MD10 MD High Income HH income>$75K 0.461 3.4 0.6694 3.7 MD1MD10 MD Shared ride dummy(car occ.>1) 0.479 3.8 0.5917 3.5 MD1MD10 MD Crossing dummy(Bridge_N > 0) 0.618 2.6 PM1PM10 PM HH size min(HH size,4) 0.05966 2.1 PM1PM10 PM High Income HH income >$75K 0.9454 5.6 PM1PM10 PM Shared ride dummy(car occ.>1) 0.6686 4.2 0.5694 4.6 PM1PM10 PM Crossing dummy(Bridge_N > 0) 0.6383 3.7 EV EV High Income HH income >$75K 0.5285 2.7 AM1AM5 AM HS Shift Early AM HH Size x (7.5t) 0.0722 4.0 AM1AM5 AM HI Shift Early AM Low Inc x (7.5t) 0.1194 2.3 AM1AM5 AM HI Shift Early AM High Inc x (7.5t) 0.1216 2.8 AM1AM5 AM BR Shift Early AM Crossing x (7.5t) 0.4295 5.8 AM6AM10 AM LI Shift Late AM Low Inc x (t7.5) 0.2483 3.8 PM1PM5 PM HI Shift Early PM High Inc x (17.0t) 0.2217 4.1