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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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46 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 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 mile of per Gross Acre within 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 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