Quantifying Transit’s Impact on GHG Emissions and Energy Use—The Land Use Component (2015) / Chapter Skim
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67 A P P E N D I X B This appendix provides full details of the statistical models used in this research. The level of detail provided here will be of interest to statisticians and modelers.
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68 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component versions. The lack of land use variables makes it impossible to use the current TTI data alone to examine the land use effects of transit on VMT.
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Statistical Models in Depth 69 Based on FHWA advice, the research team contacted individual state department of transportation offices for their shapefiles. From this effort, shapefiles for all 50 states and 443 urbanized areas were obtained.
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70 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component federal and state tax policies and regional location relative to ports of entry and refining capacity. Variables representing highway capacity and rail system capacity were also treated as exogenous, as they are the result of long-lived policy decisions to invest in highways or transit.
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Statistical Models in Depth 71 Most of the causal paths shown in the path diagram are statistically significant (have nonzero values)
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72 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component Direct Indirect Total pop 0.078 −0.025 0.052 inc 0.304 −0.015 0.289 fuel −0.448 −0.175 −0.623 hrt 0 −0.021 −0.021 lrt 0 −0.03 −0.03 flm 0.133 0.026 0.159 olm 0.04 0.131 0.172 popden −0.238 0 −0.238 rtden 0 –0.06 −0.06 tfreq 0 −0.057 −0.057 tpm −0.016 0 −0.016 Table 13. Direct, indirect, and total effects of variables on VMT per capita in the cross-sectional model for 2010 (see Figure 36)
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Statistical Models in Depth 73 VMT. The causal path through development density constitutes the land use effect of transit on VMT.
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74 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component Regression coefficients for direct causal relationships and associated significance levels are shown in Table 15. The regression coefficients give the predicted effects of individual variables on one another, all other things being equal.
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Statistical Models in Depth 75 These equations allowed the research team to estimate how the absence of transit would affect VMT for the average urbanized area. Plugging mean values for the sample into the three equations, the research team estimated a mean vmt value of 22.19, a mean tpm of 79.5, and a mean popden of 1,675.
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76 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component Cross-Sectional Analysis of Household VMT in Nine Diverse Regions This multivariate analysis pools household travel and built environment data from nine diverse regions of the United States. The model is distinct from many earlier studies for several important reasons: • Large, diverse database.
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Statistical Models in Depth 77 squares regression by accounting for the dependence of households in each region on the characteristics of that particular region, dependence that violates the independence assumption of ordinary least squares. MLM thereby produces more accurate coefficient and standard error estimates (Raudenbush and Bryk 2002)
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78 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component Houston, and Portland) with widely spaced travel surveys and with transit expansion in between the travel surveys, permitted longitudinal as well as cross-sectional analyses.
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Statistical Models in Depth 79 • Transit variables that measure the relative level and type of transit service at the neighborhood level. These are the key independent variables in the research.
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80 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component • Region size and urban form variables, accounting for regional random effects shared by all households in a given region. Statistical Methods Nesting of households within regions creates dependence among observations, in this case the dependence of households within a given region.
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vmt Fr eq ue nc y Figure 38. Histogram of household VMT.
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actden Fr eq ue nc y Figure 40. Histogram of the household buffer activity density.
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Statistical Models in Depth 83 17 For examples, see "Safe Urban Form: Revisiting the Relationship between Community Design and Traffic Safety" (Dumbaugh and Rae 2009) and "Does Street Network Design Affect Traffic Safety?
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84 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component ttrips Fr eq ue nc y Figure 43. Histogram of transit trip counts for households making transit trips.
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Statistical Models in Depth 85 Coefficient Standard Error T-Ratio P-Value constant 6.53 0.41 16.0 < 0.001 hhsize 0.506 0.039 13.0 < 0.001 employed 0.323 0.045 7.16 < 0.001 income 0.010 0.001 12.3 < 0.001 entropy −0.974 0.130 −7.57 < 0.001 intden −0.0010 0.0003 −3.08 0.003 int4way −0.013 0.002 −6.15 < 0.001 emp20a −0.010 0.004 −3.43 0.001 ttrips* −0.326 0.014 −23.1 < 0.001 lnactden*
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86 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component with increased likelihood of automobile use. The likelihood of any VMT declines with land use entropy within a 1⁄2-mile buffer around a household, with intersection density within the buffer, with the percentage of four-way intersections within the buffer, and with the percentage of regional employment accessible within a 20-minute drive time.
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Statistical Models in Depth 87 can support more intense development than can a sparse hierarchy of streets. Activity density also increases with the two exogenous transit variables, percentage of regional employment accessible within 30 minutes by transit and presence of a rail station within 1⁄2 mile of a household.
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88 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component The calculations were more complicated for transit trips. From the logistic equation in Table 22, the research team first computed the odds of any transit trips by exponentiating the log odds and then the probability of any transit trips with the formula for the probability in terms of the odds: odds of any transit trips = exp (log odds any transit trips)
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Statistical Models in Depth 89 used to obtain an estimate of household VMT due to the ridership effect only. In the third calculation, the revised value of activity density and the base value of transit trips were used to obtain an estimate of household VMT due to the land use effect only.
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90 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component assumed that changes in the treated group would have paralleled those in the comparison group in the absence of any intervention. Deviations from general trends were assumed to be due to the intervention itself -- in this case, the opening of an LRT line.
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Statistical Models in Depth 91 Variables Definition Explanation Location Household within 2 miles of a Westside LRT station or an SW Pacific Highway intersection A 2-mile buffer was used to produce a large enough sample of households for statistical purposes Household socioeconomic variables hhsize Household size Only includes household members who completed travel diaries employed Employed household members Only includes household members who completed travel diaries income Household income in 1,000s of 2012 dollars Income inflated by the personal consumption expenditure price index vehicles Household vehicles Number of cars and other vehicles owned by household Household built environmental variables actden Activity density within the 2-mile buffer in 1,000s of persons per square mile Population + employment divided by gross land area in square miles jobpop Job-population balance within the 2-mile buffer Index ranging from 0, where only jobs or residents are present within 1/4 mile, to 1, where there is one job per five residents entropy Land use mix within the 2-mile buffer Entropy index based on net acreage in different land use categories that ranges from 0, where all developed land is in one use, to 1, where developed land is evenly divided among uses intden Intersection density within the 2-mile buffer Number of intersections divided by gross land area in square miles int4way Percentage of four-way intersections within the 2-mile buffer four-way intersections or intersections where more than four streets meet divided by total intersections emp10a Percentage of regional employment accessible within a 10-minute travel time via automobile Midday travel times emp20a Percentage of regional employment accessible within a 20-minute travel time via automobile Midday travel times emp30a Percentage of regional employment accessible within a 30-minute travel time via automobile Midday travel times Household travel variables vmt Average household VMT per day Adjusted for average vehicle occupancy by household size from 2009 National Household Travel Survey wtrips Average number of household walk trips Only includes household members who completed travel diaries btrips Average number of household bike trips Only includes household members who completed travel diaries ttrips Average number of household transit trips Only includes household members who completed travel diaries atrips Number of household automobile person trips Only includes household members who completed travel diaries trips Number of household person trips by all modes Only includes household members who completed travel diaries adist Average length of automobile trips Only includes household members who completed travel diaries n Sample size Table 27. Variable definitions.
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92 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component All variables are defined and measured consistently for the two survey years. Household income is adjusted for consumer price inflation.
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Statistical Models in Depth 93 This may be the most important comparison of all, as large differences would introduce the likelihood of regression to the mean. In 1994, the two corridors were equivalent in most respects.
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94 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component Date 1994 2011 T-Ratio P-Value hhsize 2.28 2.20 −0.75 0.45 employed 1.25 1.27 0.38 0.70 income (1000s) 60.2 74.8 5.54 <0.001 vehicles 1.86 1.79 −0.95 0.34 actden (1000s)
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Statistical Models in Depth 95 achieve a good model fit. A measure of regional accessibility -- percentage of regional jobs accessible within 30 minutes by automobile -- was used to control for regional location (as opposed to local conditions)
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96 Quantifying Transit's Impact on GHG Emissions and Energy Use -- The Land Use Component N Mean Standard Deviation lnvmt 458 2.84 1.05 lnhhsize 502 0.63 0.56 employed 539 1.27 0.85 lnincome 506 11.00 0.76 emp30a (1,000s) 539 66.5 17.8 inten 539 212.0 100.4 int4way 537 22.8 19.3 ttrips 502 0.86 1.81 actden (1,000s)
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