Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 40
40 Table 2.4. Comparison of TOD housing and ITE vehicle trip generation rates: PM peak estimates. Average Rate Regression Rate TOD rate as % of Veh. Trip Rate ITE Rate ITE Rate % Below ITE ITE Rate TOD rate as % of % Below ITE (PM peak hr.) (PM peak hr.) (PM pk hr.) Rate (PM peak hr.) ITE Rate (PM pk hr.) Rate Philadelphia/NE NJ Gaslight Commons 0.460 0.67 68.66% -31.34% 0.688 66.90% -33.10% Station Square 0.558 0.67 83.25% -16.75% 0.651 85.73% -14.27% Mean 0.51 -- 75.96% -24.04% 0.67 76.32% -23.68% Std. Dev. 0.07 -- 10.32% 10.32% 0.03 13.32% 13.32% Portland, Oregon Center Commons 0.380 0.67 56.75% -43.25% 0.661 57.53% -42.47% Collins Circle 0.105 0.67 15.65% -84.35% 0.741 14.14% -85.86% Gresham Central 0.461 0.67 68.82% -31.18% 0.795 58.03% -41.97% The Merrick Apts. 0.170 0.67 25.41% -74.59% 0.695 24.51% -75.49% Quatama Crossing 0.487 0.67 72.63% -27.37% 0.625 77.91% -22.09% Mean 0.32 -- 47.85% -52.15% 0.70 46.42% -53.58% Std. Dev. 0.17 -- 25.85% 25.85% 0.07 26.32% 26.32% San Francisco Bay Area Mission Wells 0.487 0.67 72.72% -27.28% 0.645 75.56% -24.44% Montelena Homes 0.202 0.67 30.17% -69.83% 0.693 29.16% -70.84% Park Regency 0.435 0.67 64.93% -35.07% 0.621 70.10% -29.90% Verandas 0.367 0.67 54.78% -45.22% 0.662 55.43% -44.57% Wayside Commons 0.337 0.52 64.72% -35.28% 0.586 57.47% -42.53% Mean 0.37 -- 57.46% -42.54% 0.64 57.55% -42.45% Std. Dev. 0.11 -- 16.53% 16.53% 0.04 17.98% 17.98% Washington Avalon 0.370 0.67 55.26% -44.74% 0.635 58.28% -41.72% Gallery 0.234 0.67 34.89% -65.11% 0.676 34.59% -65.41% Lennox 0.220 0.67 32.90% -67.10% 0.643 34.28% -65.72% Meridian 0.056 0.67 8.33% -91.67% 0.638 8.74% -91.26% Quincey 0.201 0.67 30.06% -69.94% 0.635 31.71% -68.29% Mean 0.22 -- 32.29% -67.71% 0.65 33.52% -66.48% Std. Dev. 0.11 -- 16.69% 16.69% 0.02 17.55% 17.55% Unweighted 0.391 0.661 62.10% -37.90% 0.664 49.42% -50.58% Average Note: Fitted Curve Equation for Apartments: T = 0.60(X) + 17.52 where T = average vehicle trip ends and X = number of dwelling units Fitted Curve Equation for Condominium (Wayside Commons): T = 0.34(X) + 38.17 than the other TOD-housing projects. Omitting this single the ITE regression equation for apartments overstates traffic case improved the regression fits considerably, with respective impacts of transit-oriented housing by 39%. R-square values of 0.829, 0.800, and 0.847 for the weekday, AM peak, and PM peak. How Do Rates Vary? Using the average weekday best-fitting regression equation in Figure 2.8, the estimated number of daily vehicle trips gen- To better understand the nature of vehicle trip generation erated by a 400-unit apartment project is 1,508.3 [-523.7 + for TOD housing projects, additional analyses that explored (5.26 400) = 1,508.3]. For the same apartment land-use associations between trip generation and various explanatory category (ITE code of 220), the latest ITE Trip Generation variables were carried out. For ratio-scale variables, scatter- Manual would predict 2,554.35 daily vehicle trips for the plots and bivariate regression equations were estimated. Such same 400-unit apartment [150.35 + (6.01 400) = 2,554.35]. analyses treat every observation the same, thus the cases are un- Based on the empirical experiences of the sampled projects, weighted. For those analyses with reasonably good statistical
OCR for page 41
41 Figure 2.6. Comparison of weighted average vehicle trip rates: TOD housing and ITE estimates. 5000 400 T=-523.7+5.262X T=-31.757+0.436X 4000 R2=0.729 300 T = P.M. Peak Trip Ends R2=0.734 T = Weekday Trip Ends 3000 200 2000 100 1000 0 0 0 200 400 600 800 1000 0 200 400 600 800 1000 X = Number of Dwelling Units X = Number of Dwelling Units Figure 2.7. TOD housing weekday vehicle trip ends Figure 2.9. TOD housing PM peak vehicle trip ends by number of dwelling units. by number of dwelling units. 300 fits, cases were broken into subgroups and weighted average T=-16.774+0.327X values are presented for each category. 2 As suggested by Tables 2.2 through 2.4, the greatest variations R =0.693 in TOD trip generation rates are by metropolitan area/rail T= A.M. Peak Trip Ends 200 systems. Metropolitan Washington, with some of the nation's worst traffic conditions, most extensive modern-day railway networks, and densest (and arguably best planned) TOD hous- ing projects, had the lowest trip generation rates. This was fol- lowed by Metro Portland, whose comparatively low rates are 100 all the more remarkable given that it is smaller than the other urbanized regions and has a less extensive light rail system that operates in mixed-traffic conditions. Average trip generation rates were slightly higher for Bay Area TODs than in Portland 0 and, as noted earlier, were the highest for the Philadelphia 0 200 400 600 800 1000 and Northeast New Jersey cases, due in part to the nature of X = Number of Dwelling Units commuter rail services (focused mainly on peak periods). Figure 2.8. TOD housing AM peak vehicle trip ends TOD trip generation rates are examined as a function of: by number of dwelling units. 1) distance of project to CBD; 2) distance of project to station;
OCR for page 42
42 3) residential densities around station; and 4) parking provi- Table 2.5. Summary regression equations sions. While relationships were explored for other variables as for predicting TOD housing trip generation rates well, only these factors proved to be reasonably strong pre- as functions of distance to CBD. dictors. The analysis ends with best-fitting multiple regression equations for predicting trip generation rates of TOD housing. Period of Dependent Bivariate Equation R-Square Analysis Variable X = Distance of Project to CBD (miles) Distance to CBD Vehicle Trip Ends per 2.796 + .056X 0.097 For the weekday period, a fairly weak relationship was found Dwelling Unit Weekday between TOD housing trip generation rates and distance to (24 hours) TOD Rate as a 0.414 + .009X 0.109 the CBD. This is suggested by Figure 2.10; rates were actually Proportion of ITE Rate lower for projects more than 12 miles from the CBD than more intermediate-distance projects in the 6 to 12 mile range. Vehicle Trip Ends per 0.198 + .006X 0.156 Dwelling Unit (The >12 mile group is dominated by Bay Area cases; all five AM Peak Hour projects are more than 20 miles form downtown San Francisco.) TOD Rate as a 0.358 + .012X 0.176 Proportion of ITE Rate During peak periods, however, relationships were stronger; rates increased with distance of a project from the CBD. Vehicle Trip Ends per 0.209 + .009X 0.350 Table 2.5 summarizes the bivariate results for predicting trip Dwelling Unit PM Peak Hour generation rates as well as TOD rates as a proportion of ITE TOD Rate as a 0.309 + .015X 0.388 rates. In all cases, vehicle trip generation rates tend to rise as one Proportion of ITE Rate goes farther away from the urban core. The weakest fit was for the 24-hour period whereas the strongest was for the PM peak. The best fit was the prediction of the TOD trip generation rate equations is residential density, specifically the number of as a proportion of the ITE rate during the PM peak. That model dwelling units per gross acre within a half mile radius of the explained more than 38% of the variation in vehicle trip rates. rail station closest to the TOD housing project, estimated The scatterplot shown in Figure 2.11 reveals a fairly good fit for from the 2000 census. Residential densities were obtained this variable (based on the reasonably steep slope). from the national TOD database maintained by the CTOD. In all cases shown in Table 2.6, TOD trip generation de- clines as surrounding residential densities increase. We sus- Residential Densities pect that residential density is serving as a broader surrogate The finding that trip generation rates tend to be lower for of urbanicity, that is denser residential settings tend to have TOD housing near urban centers suggests residential density nearby retail and other mixed-use activities, better pedestrian is an important predictor. This is supported by the results connectivity, and often a more socially engaging environ- shown in Table 2.6. The predictor variable in all of these ment. Residential densities most strongly influenced PM trip Figure 2.10. Vehicle trip generation rates by distance to CBD: comparisons of weighted averages for weekday, AM peak, and PM peak.
OCR for page 43
43 T= PM peak rate as prop of ITE rate 1.0 .6 .5 T= PM peak trip generation rate .8 .4 .6 .3 .4 .2 .2 .1 0.0 0.0 0 10 20 30 0 10 20 30 X = Distance to CBD (miles) X = DU/acre within 1/2 mile of station Figure 2.11. Scatterplot of PM trip generation rate Figure 2.12. Scatterplot of PM trip generation rate to ITE rate with distance to CBD. with residential densities. Table 2.6. Summary regression equations as a function of parking per dwelling unit are presented in for predicting TOD housing trip generation rates Table 2.7. Relationships are weaker than that found for "Dis- as functions of residential densities (within 1/2 mile of stations). tance to CBD" and "Residential Densities." Vehicle trip gen- eration rates tend to be higher for TOD projects with more plentiful parking. The strongest fit was between AM peak trip Period of Dependent Bivariate Equation R-Square Analysis Variable generation and parking supply. Figure 2.13 presents the scat- X = Dwelling Units terplot of this relationship. per Gross Acre within ½ Mile of The results in Table 2.7 and Figure 2.13 are unweighted by Station project size. Figure 2.14 compares average rates for three levels Vehicle Trip Ends per 5.369 - .211X 0.430 of parking supplies, weighted by project size. No clear pattern Dwelling Unit emerges from these weighted-average results, consistent with Weekday (24 hours) TOD Rate as a 0.801 - .096X 0.424 Proportion of ITE Rate Table 2.7. Summary regression equations for predicting TOD housing trip generation rates Vehicle Trip Ends per 0.400 - .014X 0.276 Dwelling Unit as functions of parking per dwelling unit. AM Peak Hour TOD Rate as a 0.731 - .026X 0.274 Period of Dependent Bivariate Equation R-Square Proportion of ITE Rate Analysis Variable X = Parking Spaces Vehicle Trip Ends per 0.493 - .019X 0.449 per Dwelling Units Dwelling Unit PM Peak Hour Vehicle Trip Ends per 1.683 + 1.504X 0.158 TOD Rate as a 0.741 + .028X 0.423 Dwelling Unit Proportion of ITE Rate Weekday (24 hours) TOD Rate as a 0.258+ .221X 0.153 Proportion of ITE Rate generation rates among the sample of 17 TOD housing proj- Vehicle Trip Ends per 0.098 + .145X 0.206 ects. Figure 2.12 shows the scatterplot of these two variables. Dwelling Unit AM Peak Hour TOD Rate as a 0.189 + .260X 0.202 TOD Parking Supplies Proportion of ITE Rate Vehicle Trip Ends per 0.207 + .098X 0.088 Parking provisions have a strong influence on travel be- Dwelling Unit havior, particularly in suburban settings where most sample PM Peak Hour projects are located (Shoup, 2005; Willson, 1995). Bivariate TOD Rate as a 0.325 + .140X 0.078 Proportion of ITE Rate equations for predicting TOD housing trip generation rates
OCR for page 44
44 .6 Table 2.8. Summary regression equations for predicting TOD housing trip generation rates .5 as functions of walking distance to nearest station. Period of Dependent Bivariate Equation R-Square Y= AM Peak Rate .4 Analysis Variable X = Walking Distance to Nearest .3 Rail Station (in 1000s of feet) .2 Y=0.098X+0.145X Vehicle Trip Ends per 3.149 + .325X 0.027 2 Dwelling Unit R =0.206 Weekday .1 (24 hours) TOD Rate as a 0.047 + .052X 0.030 Proportion of ITE Rate 0.0 Vehicle Trip Ends per 0.209 + .060X 0.126 0.0 .5 1.0 1.5 2.0 2.5 3.0 Dwelling Unit X = Parking Spaces per DU AM Peak Hour TOD Rate as a 0.382 + .00011X 0.137 Figure 2.13. Scatterplot of AM trip generation rate Proportion of ITE Rate with parking spaces per dwelling unit. Vehicle Trip Ends per 0.249 + .071X 0.168 Dwelling Unit the fairly weak fits shown in Table 2.7. In general, trip gener- PM Peak Hour TOD Rate as a 0.374 + .00011X 0.182 ation rates were lower for TOD projects with intermediate Proportion of ITE Rate levels of parking (1.0 to 1.15 spaces per dwelling unit). This was mainly an artifact of three of these projects being in met- ropolitan Washington, D.C. half-mile of the nearest station. Figure 2.15 shows the weak scatterplot fit for the weekday (24 hour) estimate, with the Mission Wells observation (nearly 4000 feet from the station) Walking Distance to Station standing out as an outlier. Dropping this single case provides The relationship between TOD housing trip generation an appreciably better fit, as revealed in Figure 2.16. and walking distance from the project to the nearest station As Table 2.8 indicates, the strongest linear pattern between was generally weaker than the other variables reviewed so far. TOD trip rate (as a proportion of the ITE rate) and distance Table 2.8 shows a positive slope for the explanatory variable, to station was for the PM peak hour. Figure 2.17 shows this distance to station. This indicates that the closer a TOD hous- scatterplot. Retaining the Mission Wells observation, a slightly ing project is to a rail station, the vehicle trip generation rates better fit was obtained using a quadratic equation of the form: tend to be lower. The relationships were thrown off, in part, by Mission Wells, a Bay Area project situated beyond a T = 0.195 + 0.21X - 0.0000032X2 R2 = .195 Figure 2.14. Vehicle trip generation rates by parking spaces per dwelling unit: comparisons of weighted averages for weekday, AM peak, and PM peak.