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17 lyst to account for any baseline growth in the region that The analyst can also adopt a more elaborate measure of might have occurred between the "before" condition and the accessibility than the simple gravity model denominator sug- "after" condition that would have occurred with or without the gested above. Ideally, this more elaborate measure should traffic-flow improvement. be based upon some kind of trip distribution model for pre- CP is the calibration parameter that converts a percentage dicting the likelihood that a trip will be made to a particular change in zonal accessibility into a percentage change in zonal destination. growth. It is the slope of the regression line fitted to local data on the correlation between the marginal change in zonal acces- sibility and the marginal change in zonal growth expressed as 5.2 MODULE APPLICATION the sum of households and jobs. The measure of zonal accessibility (Ai ) is the denominator The Growth Redistribution Module is calibrated for each of the trip distribution gravity model for home-based work region in which it is applied. Base and future employment trips. The denominator is the sum of the weighted travel time and household forecasts are assembled for the region. A lin- impedances to each destination zone in the region. The AM ear regression model of the form shown in Equation 12 is fit- USER'S GUIDE peak-period accessibility for home-based work trips is used ted to the data to obtain the value of CP. The fitted equation as a proxy for total daily accessibility for all trips, based on is then used to predict how individual zones will deviate from the presumption that commute accessibility has the greatest the regional average growth rate based upon changes in zonal effect on housing and job location decisions. accessibility from the base condition. The following paragraphs illustrate such an application of Ai = Tj Fij Equation 13 the module to the Seattle metropolitan area. The PSRC pro- j vided household and employment forecasts for the years 1990 and 2020. These forecasts had been produced through a com- Where: bination of inventory (for 1990) and land-use modeling (using Ai = accessibility of zone i, Disaggregate Residential Allocation Model/Employment Allo- Tj = total trips generated by zone j, and cation Model [DRAM/EMPAL]) with modifications made in Fij = AM peak travel time impedance for home-based work response to local agency input. travel between zone i and zone j. Accessibility generally improved between the 1990 and 2020 PSRC forecasts; however, some zones experienced sig- The impedance is a decreasing function of travel time nificant changes in accessibility between 1990 and 2020 that between zones and takes whatever form was used to calibrate varied a great deal from the average (see Figure 4, which plots the regional travel demand model. the percentage change in accessibility for approximately the The analyst may experiment with fitting more elaborate first 790 of the PSRC zones). linear or nonlinear models to the land-use intensity forecasts. The zonal accessibilities for each mode of travel were A full-scale land-use forecasting model, like UrbanSim, reported out from the Equilibre Multimodal, Multimodal Equi- could be used instead of the simple linear model presented librium (EMME2) in which the PSRC model was imple- above. Application of a full-scale model like UrbanSim mented. The reports were then imported into a spreadsheet, would double or triple the amount of time required to ana- which was used to compute the differences between 1990 and lyze the traffic-flow improvement project. The simple linear 2020 and fit a regression line to the data. A least-squared error model was selected for the sake of efficiency, enabling more regression line was fitted to the 832 zonal data points (see Fig- rapid computations of the impacts of various traffic-flow ure 5). The line was forced through zero. The slope was 0.72, improvement projects. and the resulting correlation coefficient was 67.99 percent.
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18 500.0% 400.0% 300.0% % Change in Accessibility 200.0% 100.0% USER'S GUIDE 0.0% 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721 745 769 793 25 49 73 97 1 -100.0% Zone Figure 4. PSRC zonal accessibility changes between 1990 and 2020. 1000% Percent Change Jobs+Dwellings Beyond Average Change 800% y = 0.72x 2 R = 0.6799 600% 400% 200% 0% -100% 100% 300% 500% 700% 900% -200% Percent Change Accessibility Beyond Average Change Figure 5. Calibration of long-term module to PSRC data.