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i~c~ <br />Figure 1. The reduction in vehicle miles traveled per household as residential density increases. <br />Driving vs Residential Density <br />35000 <br />30000 <br />s 25000 <br />g 20000 <br />~ ~ , SF <br />`~ --LA <br />~ 15000 <br />~ ~~;~,~ - - -Chicago <br />Q 10000 <br />\`~+. <br />~,`~ <br />5000 ~~'`~'Y'titi'""..~..,.~..~-~_-~__-- =~ <br />-.r.......... <br />0 <br />0 50 100 150 200 <br />Households/Residential Acre <br />proximity added no significance. However, for vehicle use, center proximity added about 5% to <br />the total variation explained, giving a total RZ of 53.9% for the San Francisco region. This <br />suggests that the impacts of CP, while not measured in Chicago and Los Angeles, should be <br />further explored. The equations in Table 2 provide a high explanation of variation. <br />Figure 2 shows the impact of residential density and transit on VMT/Hh using the equation for the San Francisco <br />area. This shows that a household with regional average income and family size living in a 2 Hh/RA neighborhood <br />with zero transit service, for instance, would average over 23,000 miles annually. Raising the density to 150 <br />Hh/RA would reduce the mileage to 10,300. But it also shows that more modest increases in density reduce <br />driving. It also shows that at each level of transit service increases of density reduce driving. Similarly, it shows <br />that an increase in Tr to 300 would reduce annual driving to under 15,400. And it shows that more modest <br />increases in transit service reduce driving. At the highest density and transit service in this example, annual <br />mileage is 3,700. <br />Extending These Analyses <br />These equations allow the use of available neighborhood locational and demographic <br />Table 1 The best equations (1 - 9) to predict Veh/Hh, VMTNeh and VMT/fIlr in all three regions. <br />g,: <br />