FEIS - Tahoe Regional Planning Agency

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RESPONSE TO COMMENTS ON THE DEIS B o u l d e r B a y C o m m u n i t y E n h a n c e m e n t P r o g r a m P r o j e c t E I S (adjusted based on 2006 operating conditions), and the Alternatives. The Baseline Existing Conditions adjusted to 2006 assumes a permanent reduction due to tribal gaming of approximately 32%. The ITE Daily Trip Generation Estimates for Alternatives A, B, C, D, and E assumes that the permanent reduction due to tribal gaming is eliminated for all future conditions (e.g., the numbers represent a worst case assumption for gaming). Table 8.5-2 Trip Generation Summary including Alternative Baseline Scenario Daily Trip Generation PM Peak Existing Conditions (based on 2008 Traffic Counts) Alternative Baseline Existing Conditions (adjusted to 2006) 2,846 234 3,849 315 Alternative A 5,853 373 Alternative B 7,870 504 Alternative C 3,501 274 Alternative D 3,948 302 Alternative E 8,468 554 Source: Fehr & Peers, 2010 Please refer to the Boulder Bay Alternative Baseline Existing Conditions Traffic Volumes (Fehr & Peers, 2010) in Appendix AA of the <strong>FEIS</strong> for additional information on the methodology for calculating the alternative baseline existing condition. As shown in Table 8.5-2, using the results of the alternative baseline conditions analysis, the Proposed Project (Alternative C) generates more traffic than the 2008 counts, but less daily traffic than the alternative baseline conditions. Alternatives C and D also generate less PM peak hour traffic than the alternative baseline existing conditions. The alternative baseline existing conditions traffic analysis supports the conclusions included in the DEIS that Alternative C would not increase vehicle trips. Master Response 3 – Internal/External Alternative Mode Trips, Fehr & Peers Mixed Use Development Model Comment Summary – The EIS should explain why Alternatives C and A have different internal trip capture rates. The Fehr & Peers mixed-use development trip generation model is inaccurate because it does not include interval-ownership or hotels, it fails to consider external trips by guests to recreation, retail, and services beyond the Project Area, and trips were double counted when “Alternative Mode Split” factors were applied to the total number of “raw” trips, rather than only to external trips. Trip reduction estimates based on the shuttle service are overly optimistic. PAGE 8- 18 HAUGE BRUECK ASSOCIATES SEPTEMBER 8 , 2010
RESPONSE TO COMMENTS ON THE DEIS B o u l d e r B a y C o m m u n i t y E n h a n c e m e n t P r o g r a m P r o j e c t E I S Fehr & Peers Mixed Use Development Model Methodology The Fehr & Peers mixed-use development model was developed using data from 239 mixed-use developments in six metropolitan regions (Boston, Atlanta, Houston, San Diego, Seattle, and Sacramento). While the data were collected in urban areas, the relationship between mixed uses can be applied to mixed-use projects in less urban areas. Hierarchical Linear Modeling (HLM) techniques were used to quantify relationships between characteristics of the mixed-use developments and the likelihood that trips generated by those mixed use developments will be made by means other than the private automobile. The mixed-use development model calculates the number of Alternative mode trips (trips made by walking, bicycling, transit, etc.) and determines the split between internal (walking between uses on the site) and external (walking, bicycling, or taking transit to a use off the site) Alternative mode trips. The Fehr & Peers mixed-use development model considers the following variables when analyzing mixed-use developments: • Employment • (Population + Employment) per square mile • Land Area • Total Jobs / Population Diversity • Retail Jobs / Population Diversity • Number of intersections per square mile • Employment within a mile • Employment within a 30-minute trip by transit • Average Household Size • Vehicles Owned Per Capita Many of these variables are examples of the "Ds", which are built environment variables that are known to influence travel behavior - density, diversity, development scale, design, and distance to transit. Validation A set of 16 independent mixed-use sites that were not included in the initial model was tested to help validate the model. The actual observed trip generation of the 16 test sites were compared to the trip generation estimated by the model. The model produced superior statistical performance when comparing the model results to observed data. Specifically, the trip generation estimated by the mixed-use development model better replicated the observed trip generation at the 16 test sites than trip generation estimated using the traditional ITE methods. For example, the statistical analysis comparing the observed trip generation to trip generation estimates from the traditional ITE methodology indicated an R-squared value of 0.58 (meaning the methodology explains about 58 percent of the variability in trips). The statistical analysis comparing the observed trip generation to trip generation estimates using the mixed-use model indicated an R-squared value of 0.82 (meaning the methodology explains 82 percent of the trips). The mixed-use development model has been developed in cooperation with the US Environmental Protection <strong>Agency</strong> (EPA) and ITE. ITE is currently reviewing the model for potential inclusion in their updated recommended practice for evaluating mixed-use development SEPTEMBER 8 , 2010 HAUGE BRUECK ASSOCIATES PAGE 8- 19
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FEIS - Tahoe Regional Planning Agency

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