Guidelines for Analysis of Investments in Bicycle Facilities (2006) / Chapter Skim
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Appendix B: Bicycling Demand and Proximity to Facilities
Appendix B: Bicycling Demand and Proximity to Facilities
Pages 66-72
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From page 66... ...
does having neighborhood retail within walking distance increase the propensity to complete a walk trip from home? The primary advantage of this work is that it carefully analyzes these relationships for an urban population employing detailed GIS/urban form data and a robust revealed preference survey.
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From page 67... ...
We additionally linked households with neighborhood spatial attributes relative to their reported home location. We selected all subjects from the TBI diary database who were residents of Minneapolis or St.
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From page 68... ...
and home location of walkers (below)
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From page 69... ...
These covariates represent factors that may differ across exposure levels and thus could potentially confound our effect estimates. To help free our estimates from confounding explanations we use these covariates to statistically equate subjects on observed characteristics across exposure groups; therefore, the only measured difference between them is the proximity to either the retail or the bicycle facilities.
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From page 70... ...
For example, subjects living in close proximity to any bicycle facility are more likely to be 40 or older, have a college degree, and live in households with no children than subjects living farther away from a bike facility. Different covariate patterns emerge depending upon which proximity measure we examine.
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From page 71... ...
In the simple logistic regression model (Model 1a in Table 12) , subjects living within 400 meters of an onstreet bicycle facility had significantly increased odds of bike use compared with subjects living more than 1,600 meters from an on-street Bicycle Use Walk use Model 1a Model 1b Model1c Model 2a Model 2b Model 2c Distance to nearest on-street bicycle path Distance to nearest retail establishment < 400 meters 2.933 3.101 2.288 < 200 meters 3.098 3.060 2.348 (3.11)
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From page 72... ...
CONCLUSION This research reports the results of individual level models predicting bicycling and walking behavior and correlations with proximity to bicycle paths and neighborhood retail, respectively. We do so focusing on particular behavior -- whether an individual biked or walked from home -- and robustly measuring policy relevant dimensions of the built environment.
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