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Site Design," in the case study, "San Francisco East Bay Pedestrian Versus Auto Oriented
Neighborhoods." The two neighborhoods examined are both centered on BART HRT stations
located on the same line and have essentially the same BART commute mode share at roughly
20 percent. (Rockridge also has a 5 percent bus commute share which Lafayette lacks.) The
neighborhoods each extend beyond the immediate station areas within which a true TOD would
be located. Rockridge features a pedestrian-friendly streetcar-suburb design with small blocks
and a commercial area with sidewalk storefronts. Lafayette, in contrast, is basically auto-
oriented in layout. In Rockridge, 31 percent of residents who ride BART walk to the station,
versus 13 percent in Lafayette. The Rockridge bus access share is also higher, and the BART
station auto access share for Rockridge residents is 56 percent, versus 81 percent for Lafayette
(Cervero and Radisch, 1995).
Such potential relationships with prevalence of walking to the station were explored in the 2003
California TOD travel characteristics study. However, a non-motorized-access research model esti-
mated by that study actually identified only one neighborhood design variable as having signifi-
cance. Street lighting intensity on the shortest route to the station was found to have a positive
influence on the choice of non-motorized travel (NMT--walk and bike) for station access. Higher
income was also positively related to choice of NMT modes, while higher auto ownership was a
negative factor (Lund, Cervero, and Willson, 2004a).
Automobile Ownership
Many studies recognize automobile ownership to be a key factor in mode choice. Individuals living
in households without an automobile, or with less autos than licensed drivers, are simply much
more likely to use transit, walk, or rideshare than individuals living in households with more
automobiles. Automobile ownership levels among station-area residents have been seen to be
lower as compared to non-station-area residents. To a degree, this may be an outcome of a number
of the other underlying traveler response factors for travel behavior associated with TODs, such
as land use and site design, parking policy and pricing, self-selection of residents, and transit
service quality.
Three California studies report on the association of vehicle ownership and travel behavior at
TODs. Strong associations between vehicle ownership levels and mode choice, specifically within
the immediate surroundings of rail transit stations, were found by the 2003 California TOD travel
characteristics study. For example, among surveyed station-area residents with no household vehi-
cle available, 79 percent of all trips were made by transit. In contrast, residents with one vehicle
available had a 27 percent transit share, and residents with two-or-more vehicles had a 10 percent
transit share.
The multiple-regression model developed to investigate influences on transit mode share for all
trips made by station-area residents, discussed above in connection with station-area design and
displayed in Table 17-28, also highlights the importance of vehicle ownership. The research model
shows transit mode share to decrease by 23.3 percentage points for every additional vehicle per
adult household member, all else being equal (Lund, Cervero, and Willson, 2004a).
A study using the 2000 Bay Area Travel Survey developed several research models, including
models addressing commute mode choice, residence location choice, and automobile
ownership. The modeling effort is further discussed later in this section under "Self-Selection
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of Residents." Household vehicle count was the strongest lever in the commute mode choice
model, especially when looking at the difference between households with zero and one car. The
model also showed that station-area residents and workers had greater probability of using rail
transit for commuting than non-station-area residents or workers, all other things being equal
(Cervero and Duncan, 2002). Figure 17-5 illustrates the sensitivity, in the model, of rail mode
choice to car ownership, residence location, and workplace location.
Figure 17-5 Sensitivity to vehicle ownership, residence location, and workplace location of
commute trip rail mode choice in the San Francisco Bay Area
Source: Cervero and Duncan (2002).
The research model set's multinomial-logit car ownership model is displayed in Table 17-29.
It predicted lower car ownership for households with a station-area residence or workplace, with
having a station-area residence about twice as influential as having a station-area workplace. Car
ownership was also tied to two wealth indicators: income and home ownership. Lower income
households were more likely to own fewer cars than higher income households. Households that
owned their residence were more likely to own more cars than households that rented. Larger
households were more likely to own a car, or two-or-more cars, than smaller households. Transit
job accessibility, as defined in Table 17-29, was found to have a negative (inverse) relationship with
car ownership (more transit job accessibility = fewer cars owned). Conversely, highway job acces-
sibility was found to have a positive, though less strong, relationship (Cervero and Duncan, 2002).
An example of direct empirical evidence of lower auto ownership within station areas is
provided by studies made in the Vancouver, British Columbia, region. Surveyed were some
4,000 households in 60 buildings around 6 stations on the Skytrain system, Vancouver's
Advanced Light Rail Transit rapid transit service with many HRT operating characteristics.
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The households near stations were found to use transit much more than households further away.
They also owned 10 percent fewer vehicles. Most dramatic, however, was the auto ownership rela-
tionship to transit use. Frequent Skytrain users owned 29 percent fewer vehicles than households
making less-frequent use of the service. These relationships notwithstanding, station proximity
was found to be less important than lower household income, smaller household size, and smaller
dwelling unit size as a predictor of lower auto ownership (Boroski et al., 2002).
Table 17-29 Multinomial-Logit Model for Predicting Household Car Ownership
1 Car in Household 2+ Cars in Household
Variable Coef. T-stat Coef. T-stat
Location Attributes
Reside within 0.5 miles of station (0-1) -0.943 35.14 -1.717 102.08
Work within 0.5 miles of station (0-1) -0.429 8.193 -0.890 33.22
Job accessibility index, auto network:
Jobs (in 100,000s) within 30 minutes of 0.031 1.98 0.032 2.02
residence
Job accessibility index, transit network:
Jobs (in 100,000s) within 45 minutes of -0.191 1.70 -0.250 2.73
residence
Household Attributes
Household size, number of persons 0.114 1.94 1.071 170.02
Lower-income household: <$40,000
-2.031 56.16 -3.961 208.08
annual income (0-1)
Middle-income household: $40,000 to
-0.871 9.92 -1.952 50.02
$75,000 annual income (0-1)
Own Residence (0-1) 0.881 22.47 2.005 114.58
African-American householder (0-1) -0.376 2.59 -1.183 22.40
Constant 4.004 137.31 2.796 66.22
Number of cases 2,760 9,696
Notes: Model predicts 1 car and 2+ car ownership with 0 car category suppressed.
Parenthetical "(0-1)" indicates that "no" is entered as "0" and "yes" as "1".
Source: Cervero and Duncan (2002).
Household size influences on auto ownership were further examined in a California TOD
study involving surveys of residents in 12 HRT station areas. Only 30 percent of station-area
households owned two cars as compared to 52 percent of all households in the same census
tracts. Households in station areas likewise owned fewer vehicles overall (1.26 vehicles
per household on average) as compared to all households (1.64 vehicles per household).
This represents a rate of auto ownership around stations that is 23 percent less, a finding with
parking requirement implications. The direction of causality was not determined, with no
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