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I N N O VAT I V E M E T H O D S F O R P R I C I N G S T U D I E S 145
We estimated new MNL models for the six trip pur- income jobs and are more likely to occur in off-peak
pose and direction combinations, using 32 alternatives. periods.
Compared with the remainder of the modeling system, · Household size--Larger households are less likely
the a.m. and p.m. peak periods are both expanded to to travel to work in the a.m. peak than smaller house-
include wider shoulder periods. The a.m. peak, midday, holds, as indicated by the negative and significant house-
and p.m. peak periods are set to be 5 h long and contain hold size coefficient in the HW model. It may be that
ten half-hour subperiods. The evening and night periods larger household sizes indicate the presence of children
remain as single periods, spanning 3 and 6 h, respectively. or more complicated household structures, which, com-
We use period-specific variables and shift variables to bined with multiple workers in the household, lead to
move trips earlier or later within each of the three larger flexible or extended work schedules resulting in more
periods. The shift variables are nonlinear; for example, it reverse direction work trips. By contrast, smaller house-
may take more than twice as much a.m. peak congestion holds are more likely to return home from work in the
to get someone to shift their departure time 30 min ear- p.m. peak period, as indicated by the negative and sig-
lier than it does to get the same person to shift 60 min nificant coefficient in the WH model. It is possible that
earlier. smaller households have fewer outside constraints on
The following variables were tested in the model esti- work hours and schedules, and work trips can occur in
mation: sociodemographic (i.e., income and household more traditional work hours.
size); land use and accessibility (i.e., total employment · Carpool dummy--If a WH trip is made using the
accessible by auto within 6 min and retail employment carpool mode of travel, then this variable is equal to 1;
accessible by auto within 15 min); and origindestination otherwise, it equals 0. The coefficient of this variable is
and level of service (i.e., auto in-vehicle generalized cost very significant and positive in the HW model, and very
in minutes during each of the five periods, bridge dummy significant and negative in the WH model. This coeffi-
variable, and shared ride dummy variable). cient is negative in the WH model, indicating that car-
All the variables that were tested and either retained or pool trips from work to home are less likely to occur,
excluded in the utility equation for the logit choice mod- and it is hypothesized that there are fewer opportunities
els were based on their significance, whether the sign of for casual carpooling from work to home than vice versa.
the coefficient was logical, and whether the data can be For nonwork trips, this is significant and positive in both
forecasted by the metropolitan planning organization or directions of the trip except for trips returning home dur-
the DOT. Variables that were tested but not included in ing the a.m. peak period where it is negative because car-
the final models are the employment accessibility vari- pooling is usually not an option from a nonhome
ables. A description of the variables retained in the final nonwork location.
models and the impact of these variables on the temporal · Bridge dummy--If a trip is made using one of three
choice behavior of travelers are as follows: bridges in the Puget Sound region (namely Tacoma Nar-
rows, I-90, and SR 520), then this variable is equal to 1;
· Household income--The dummy variable that and, if not, it is 0. In the HW model, this coefficient was
indicates high-income group (>$75K) has a significant significant and positive in the a.m. peak period, indicat-
coefficient specific to p.m. peak period in the WH model ing that it is more likely that trips across the bridge will
whereas in the HW model, the coefficient is significant be made during morning peak hours solely for work-
in the a.m. peak period. This indicates that commuters related purposes. These coefficients were more signifi-
from higher-income households are more likely to travel cant in the midday and p.m. peak periods of the WH
to work during the a.m. peak period and less likely to model, indicating a higher likelihood of trips across
travel during the p.m. peak period. This is further cor- bridges in the reverse work commute direction. This was
roborated in the WH model where the income coeffi- found to be significant and positive in the nonwork mod-
cient for the a.m. period was insignificant (and not els during the midday period, indicating the propensity
included), suggesting that commute trips from higher- of nonwork travelers to opt for uncongested periods to
income households are not as likely to be destined to perform nonwork activities.
home during the morning peak period. The income coef- · Congestion level--The level of congestion or delay
ficients for the p.m. time period are greater than for is measured by the difference in generalized cost (in min-
other time periods in the WH model, indicating that utes) for a.m., midday, p.m., and evening time periods
higher-income commuters are more likely to return and the generalized cost for nighttime period. This vari-
home during the p.m. peak period. The lower-income able is found to be negative and significant in all models,
variable (<$45K) has a negative coefficient in the a.m. indicating that delay affects travel decisions by time-of-
peak period of the HW model, probably because lower- day choice significantly. The size of the coefficient in the
income jobs have more irregular hours than high- HW model is less negative than in the WH model, indi-
