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28 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
has fewer trips identified as work or school trips and absolute trip-making rates were different due to trip gen-
many more identified as non-home-based trips. eration issues described earlier.
A relative comparison for trip distribution was the When the 2030 distributions of the two models were
summary of employment attracted to each zone as part compared, larger differences emerged. Compared with
of the work tour primary-destination choice model. This its base year forecast, SF-CHAMP showed a small reduc-
comparison required the estimation of non-San Fran- tion in intradistrict movements for all quadrants except
cisco residents who work in San Francisco by zone, the Sunset, the lowest-density and most suburban car-
which, to some degree, may have biased the comparison oriented part of the city. The Sunset district was the only
results. Another comparison was the trip table at the quadrant that increased its share of trip making to and
district-to-district level for intracounty trips; this table from all other quadrants, by up to four percentage
showed a strong correlation in percentage distribution of points. No district-to-district movement changed by
trips by district between the San Francisco and MTC more than four percentage points when the base was
models but a difference in total trips due to the underes- compared with the 2030 forecast with SF-CHAMP.
timation of trips discussed in trip generation. The MTC model showed larger swings in trip distri-
Trips by mode and superdistrict showed a strong sim- bution in a somewhat similar pattern. Again, the Sunset
ilarity between the results of the mode shares by district showed growth, but the MTC model also pre-
superdistrict, which resulted from the fact that both dicted a relative increase in trips to downtown and an
mode choice models were developed from the same 1990 increase in intradowntown trips. These data contra-
MTC travel survey data. A comparison of the vehicle dicted the SF-CHAMP's 2% reduction in trips to down-
trips showed there is a significant difference between the town. This finding echoed other studies that have found
trip-based and the tour-based auto mode shares. Drive- the gravity model used in trip-based distribution models
alone trips are slightly overestimated in SF-CHAMP, and to be quite sensitive to changes in travel time.
carpool trips are underestimated compared with the From the perspective of mode split, the two models
MTC model. A comparison with the Census Transporta- behaved in similar manners in the base and future years.
tion Planning Package (CTPP) for trips within San Fran- In both the base year and 2030, the SF-CHAMP model
cisco showed that drive-alone trips were 89% of total predicted more walk trips, fewer transit trips, and more
vehicle trips and shared ride trips were 11% of total vehi- drive trips than did MTC. The relative size and direction
cle trips, which bore a strong correlation to the San Fran- of these differences was about the same in both base and
cisco model results. future years, except for walk trips.
Forecast Year 2030 MODEL APPLICATIONS
MTC produced Year 2030 forecasts for its regional Equity Analysis
transportation plan. The SF-CHAMP model used the
same land use projections, road improvements, and SFCTA developed an application of the San Francisco
regional transit improvements as the MTC model. This tour-based model to estimate impacts on mobility and
consistency allowed for convenient comparison of results accessibility for different populations so as to support
from the mode choice steps of each model. development of a countywide transportation plan (4).
The overall trip rates per household remained similar Equity analyses based on traditional travel demand fore-
in the 2030 forecasts for both models: about 9.2 trips cast models were compromised by aggregation biases
per household. As in the base case, for the two models, and data availability limitations. Use of the disaggregate
the distribution across the various trip purposes was dif- (individual person-level) San Francisco microsimulation
ferent, due again to the impact of intermediate stops; the model made it possible to estimate benefits and impacts
MTC model predicts more home-based trips, particu- to different communities of concern on the basis of indi-
larly work trips, and fewer non-home-based trips than vidual characteristics such as gender, income, auto avail-
the SF model. This accounting issue is well understood. ability, and household structure.
Examination of the geographic distribution of trips
revealed more differences. In the base year, the San Fran-
cisco and MTC models predicted similar overall levels of Tenderloin Residents
trip-making among the four quadrants (defined by MTC
as "superdistricts") of San Francisco; comparison of the A recent study of the predominantly low-income Tender-
trip distribution patterns for the San Francisco and MTC loin neighborhood took advantage of disaggregate
models showed that all movements between all superdis- model outputs to explore the differences between travel
tricts varied by less than 3% on relative terms. Again, the patterns of Tenderloin residents and other trip makers in
