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45
7 1.0
T= PM peak rate as prop of ITE rate
6 Y=3.15+0.00032X
Y= Vehicle Trip Rate (24 hours)
.8
2
5 R =0.027
.6
4
3
.4
2
.2
1
0 0.0
0 1000 2000 3000 4000 0 1000 2000 3000 4000
X = Distance to Station (Feet) X = Distance to Station from Project (feet)
Figure 2.15. TOD housing vehicle trip rates Figure 2.17. TOD-housing vehicle trip rate
by shortest walking distance to station; N = 17 (as a proportion of ITE rate) by walking distance
(all cases). to station; quadratic curve; N = 17.
7
bivariate relationships between TOD trip generation and
6 other explanatory variables (such as compiled in the pedes-
Y= Vehicle Trip Rate (24 hours)
trian survey and through the CTOD database) were very weak
5 and statistically insignificant.
This section presents a multiple regression equation that
4
combines explanatory variables to produce the best-fitting
predictive models. These results provide insight into how
3 Y=2.37+0.0012X other factors combine with proximity of multi-family
2
2
R =0.127 housing to rail stations to influence vehicle trip generation
rates.
1
0
Weekday TOD Trip Generation Model
0 500 1000 1500 2000 2500 3000
The simple bivariate models shown in Table 2.6 pro-
X = Distance to Station (Feet)
vided the best fit for predicting weekday TOD trip genera-
Figure 2.16. TOD housing vehicle trip rates tion rates (as well as rates as a proportion of the ITE rate).
by shortest walking distance to station, That is, once controlling for residential density around the
without Mission Wells Case; N = 16. station, none of the other variables--walking quality, parking
supply, socio-demographic characteristics of the surround-
where T is TOD-housing PM trip rate as a proportion of ITE ing neighborhood--provided significant marginal explana-
rate and X is the walking distance of project to the nearest sta- tory power. Again, density is thought to function as a proxy
tion (in 1,000s of feet). for many of these factors. The finding that walking quality
has little bearing on vehicle trip generation rate also is con-
sistent with research findings from California (Lund, et al,
Multiple Regression Predictions
2004). That work suggested the presence of an indifference
of TOD Housing Trip Generation
zone; as long as most residents were within five or so min-
Rates
utes of a station, walking quality matters relatively little. The
The previous section found modest to moderate rela- presence of an integrated sidewalk network, street trees, and
tionships between TOD housing trip generation rates and various pedestrian amenities likely have more influence on
four variables: distance to CBD, residential density, parking longer-distance walking behavior than encountered by most
per dwelling unit, and distance to station. In general, the TOD residents.
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Model 1: TOD Trip Generation Model Model 3: TOD Trip Generation Model
for the AM Peak for the PM Peak
In predicting trip rates for the morning peak hour, the below A better fitting model was obtained for predicting TOD trip
output reveals that trip generation falls with residential densi- generation in the afternoon peak (Table 2.11). The results,
ties and increases with project parking supplies (Table 2.9). which explained 60% of the variation in PM trip rates, reveal
The combination of higher densities and lower parking sup- that vehicle travel in the afternoon rises with distance to the
plies holds promise for driving down morning vehicle trips for CBD and falls with both residential density and household size.
transit-based housing. The parking variable, however, is not
statistically significant at the 0.10 probability level.
Model 4: TOD Trip Generation Model
for PM Peak (as a Proportion of ITE Rate)
Model 2: TOD Trip Generation Model for AM Peak
(as a Proportion of ITE Rate) The best-fitting multiple regression equation was pro-
duced for predicting PM peak trip rates as a proportion of
Comparable results were found for predicting AM peak
ITE rates (Table 2.12). This model explained 63% of the vari-
rates as a proportion of the ITE rate (Table 2.10).
ation. Like the previous model, this one showed that TOD
projects closest to the CBD, in higher density residential set-
Table 2.9. Best-fitting multiple regression equation tings, and in neighborhoods with smaller household sizes
for predicting AM peak trip generation rates
averaged the lowest PM trip rates.
for TOD housing projects.
Using the best-fitting multiple regression model for the
AM Peak Rate
PM peak, Figure 2.18 reveals how PM trip rates for the TOD
projects differ as a proportion of the rates predicted by the
Std. ITE manual. Assuming an average household size of two per-
Coeff. Err. t Statistic Prob.
sons, the predicted values as a function of distance to CBD
Residential Density: Dwelling Units (horizontal axis) and residential densities (within half mile of
per Gross Acre within ½ mile of
station -0.012 0.006 -1.961 .075 the nearest rail station, represented by the five bars) are
shown in the Figure. For example, the model predicts that for
Parking Supply: Parking Spaces per
Dwelling Unit 0.106 0.070 1.507 .154 a transit-oriented apartment 20 miles from the CBD in a
neighborhood with 10 units per residential acre, the PM trip
Constant 0.250 0.116 2.152 .039
rate will be 55% of (or 45% below) the ITE rate. If the same
Summary Statistics: apartment in the same density setting were 5 miles from the
F statistics (prob.) = 3.800 (.048) CBD, the PM trip rate would be just 38% of the ITE rate. For
R Square = .352
Number of Cases = 17
Table 2.11. Best-fitting multiple regression equation
for predicting AM peak trip generation rates for TOD
Table 2.10. Best-fitting multiple regression equation
housing projects.
for predicting AM peak trip generation rates as a
proportion of ITE rate for TOD housing projects.
AM Peak Rate
AM Peak Rate Std.
Coeff. Err. t Statistic Prob.
Std.
Coeff. Err. t Statistic Prob. Distance to CBD (in miles) 0.007 0.003 2.145 .051
Residential Density: Dwelling Units Residential Density: Dwelling Units
per Gross Acre within ½ mile of per Gross Acre within ½ mile of
station -0.021 0.011 -1.948 .072 station -0.018 0.006 -2.846 .014
Parking Supply: Parking Spaces per Household Size: Persons per
Dwelling Unit 0.189 0.128 1.484 .160 Dwelling Unit within ½ mile of station -0.103 0.074 -1.390 .188
Constant 0.462 0.210 2.196 .045 Constant 0.608 0.182 3.346 .005
Summary Statistics: Summary Statistics:
F statistics (prob.) = 4.154 (.038) F statistics (prob.) = 6.497 (.006)
R Square = .372 R Square = .600
Number of Cases = 17 Number of Cases = 17
