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EFFECT OF TRAILS ON CYCLING

Research supports the theory that the built environment matters; however, it suggests that one needs to live extremely close to such facilities to have an statistically significant effect.

From the Twin Cities Metropolitan Area Travel Behavior Inventory (2000)

Map of Minnesota
photo: Along the Mississippi River Trail in Minneapolis

Along the Mississippi River Trail in Minneapolis

 

The work aims to answer the following questions:
(a) does having a bicycle lane/path close to home increase the propensity to complete a cycling trip, and
(b) 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. The study uses multivariate modeling techniques to estimate the effect of features of the built environment on outcomes related to bicycling and walking.

The results suggest that distance to these facilities is statistically significant; however, the relationship is not linear. The most important point is that close proximity matters, which challenges conventional wisdom that people are willing to walk up to a quarter mile as well as analogous cycling-specific hypotheses. These results are not overly promising for planners and advocates; but this work raises a number of important data, measurement, and methodological issues for future researchers endeavoring to predict levels of walking or bicycle use for entire cities or metropolitan areas.

"Even among the urban population, only five percent cycled and twelve percent walked."

Our knowledge of who walked and cycled is derived from a home interview survey known as the 2000 Twin Cities Metropolitan Area Travel Behavior Inventory (TBI). This survey captures household travel behavior and socio-demographic characteristics of individuals and households across the seven-county metropolitan area.

Our exposures of interest vary for each mode and are based on distance, which is often mentioned as a suitable measure of impedance. For cycling, our exposure is the proximity of bicycle facilities in the form of on- and off-street bicycle lanes and trails. Three continuous distance measures were calculated using GIS layers furnished by the Minnesota Department of Transportation. Combining this data with precise household locations, we calculated the distance in meters to the nearest on-street bicycle lane, the nearest off-street trail, and the nearest bike facility of either type. Four distinct categories represent the distance from one's home to the nearest bicycle trail as < 400 meters (one quarter mile), 400-799 meters, 800-1599 meters, and 1600 meters or greater (greater than one mile).

We identify several covariates to represent individual, household, and other characteristics. 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 each of the exposure levels.

For individual characteristics, we use age, gender, educational attainment (college degree or not), and employment status (employed or not). For household characteristics, we use 4 household income (five categories), household size, and whether the household had any children less than 18 years old. We also use two other measures: household bikes per capita and household vehicles per capita. We calculate these by dividing the total number of bicycles by household size and dividing the total number of vehicles by household size.

The specific outcomes of interest in this application are twofold; both were operationalized in a dichotomous manner. The first is whether the respondent completed a bicycle trip as documented in the 24-hour travel behavior diary. A total of 5.2% reported doing so. This rate is higher than both the larger TBI sample and national averages, which tend to hover around 2% of the population. The second outcome of interest was if they had a walking trip from home, which comprised 12.4% of our sample.

Our first models explore the odds of bicycle use and proximity to any type of bicycle facility. From the simple logistic regression model to the fully adjusted model, the odds of bike use did not differ significantly by proximity to any bike facility. Our model suggests that there is no effect of proximity to any bike facility on bike use. We therefore used a separate model to estimate the effect of proximity to off-street facilities on the odds of bike use. Examining the simple logistic regression model to the fully adjusted model for off-street bicycle facilities, the odds of bike use did not differ significantly by proximity to a trail. We detected no effect of proximity to off-street bike facilities on bicycle use.

Finally, we examined the effect of proximity to on-street bike facilities on the odds of bike use. In the simple logistic regression model (Model 1a in Table 1), subjects living within 400 meters of an on-street bicycle facility had significantly increased odds of bike use compared with subjects living more than 1600 meters from an on-street bike facility.

As expected, those that lived within 400 to 799 meters of an on-street bike facility also had significantly increased odds of bike use compared with subjects living more than 1600 meters from an on-street bike facility, although the odds of bike use were slightly lower than for those living closest to an on-street facility.

After adjusting for individual and household characteristics, the effects were somewhat attenuated. Subjects living in close proximity to an on-street facility (< 400 meters) still had statistically significantly increased odds of bike use compared with subjects living more than 1600 meters from an on-street bike facility. However, subjects within 400 to 799 meters still tended toward increased odds of bike use, however this failed to reach the level of statistical significance.

Somewhat to the chagrin of many officials excited about the prospects of using community design to induce physical activity, this analysis suggests an uphill battle lies ahead. First, our results underscore the fact that we are addressing fringe modes and rare behavior. Even among the urban population, only five percent cycled and twelve percent walked. And, the criteria for satisfying this measure were generous— any cycling or walking trip from home that was reported by the individual over a 24 hour period.

Second, the research supports the theory that the built environment matters; however, it suggests that one needs to live extremely close to such facilities to have an statistically significant effect (i.e., less than 400 meters to a bicycle trail for bicycling, and less than 200 meters to retail for walking— approximately the length of two football fields). While the odds-ratios for longer distances failed to reach levels of statistical significance, it is important to mention that in all model estimations, they were always in decreasing orders of magnitude and always in the assumed direction. Planners need to be aware of such distance considerations when designing mixed land use ordinances.

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