Cycling to work in 90 large American cities: new evidence on the role of bike paths and lanes

Abstract

This article analyzes the variation in bike commuting in large American cities, with a focus on assessing the influence of bike paths and lanes, which have been the main approach to increasing cycling in the USA. To examine the role of cycling facilities, we used a newly assembled dataset on the length of bike lanes and paths in 2008 collected directly from 90 of the 100 largest U.S. cities. Pearson’s correlation, bivariate quartile analysis, and two different types of regressions were used to measure the relationship between cycling levels and bikeways, as well as other explanatory and control variables. Ordinary Least Squares and Binary Logit Proportions regressions confirm that cities with a greater supply of bike paths and lanes have significantly higher bike commute rates—even when controlling for land use, climate, socioeconomic factors, gasoline prices, public transport supply, and cycling safety. Standard tests indicate that the models are a good fit, with R 2 ranging between 0.60 and 0.65. Computed coefficients have the expected signs for all variables in the various regression models, but not all are statistically significant. Estimated elasticities indicate that both off-street paths and on-street lanes have a similar positive association with bike commute rates in U.S. cities. Our results are consistent with previous research on the importance of separate cycling facilities and provide additional information about the potentially different role of paths vs. lanes. Our analysis also revealed that cities with safer cycling, lower auto ownership, more students, less sprawl, and higher gasoline prices had more cycling to work. By comparison, annual precipitation, the number of cold and hot days, and public transport supply were not statistically significant predictors of bike commuting in large cities.

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Fig. 1

Notes

  1. 1.

    The western Census region includes Alaska, Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

  2. 2.

    Compact land use is measured by a composite ‘sprawl index’ with lower values for sprawled development and higher values for compact development as explained in detail later in the text.

  3. 3.

    Seven cities reported 0 miles of bike lanes or bike paths. These cities would have been lost in our models, because the natural logarithm of 0 is not defined. Thus, we followed the common procedure of transforming the bike lane and path per 100,000 population variable by adding 1, which yields a log value of 0 for the 7 cities. We also estimated the models without this transformation, with only 83 cities. Significance, sign, and magnitude of coefficients and goodness of fit were very similar to the results of the models presented in this paper.

  4. 4.

    Variance Inflation Factor (VIF) yields scores for individual variables below 2.7 and a score of 1.9 for the overall equation. Tolerance values are all above 0.4.

  5. 5.

    A possible reason for this low correlation may be that state cyclist fatality rates are imperfect proxies for city fatality rates.

  6. 6.

    For an alternative approach to estimating fractional response variables using a so-called ‘quasi-likelihood estimation method,’ see Papke and Wooldridge (1996).

  7. 7.

    In an attempt to model the simultaneous dependencies among the variables, we experimented with several alternative instrumental variables to estimate a simultaneous equation system using two-stage regressions. Unfortunately, none of the available variables in the dataset were sufficiently exogenous or strong enough to serve as instrumental variables. They failed on one or more criteria required for statistically robust and valid instrumental variables: (1) underidentification (Anderson LM statistic), (2) weak identification (Cragg–Donald Wald F statistic), (3) overidentification (Sargan statistic), (4) or robust instrument inference (Anderson–Rubin Wald test). The best instrumental variable in the dataset was city land area—since area is fully exogenous and correlated with the total number of bike commuters and the extent of bike paths and lanes. The technical estimation procedure of two-stage least squares (2SLS) required combining the length of bike paths and lanes into one variable, because there was only one instrumental variable available. Moreover, the model was re-specified with the log of total number of bike commuters as dependent variable and the log of total length of bike paths and lanes as regressor. This model satisfied most of the statistical tests for appropriateness of the instrument, but failed to reject the null hypothesis of the Sargan test for overidentification—which casts some doubt on the validity of the instrument.

    Estimating a 2SLS equation with this imperfect instrumental variable yields results for the bikeway variable that are similar to those for an OLS regression. In the 2SLS model, bike paths and lanes are statistically significant predictors of cycling levels—even after accounting for endogeneity bias. Another instrumental variable we examined—measuring city population per bicycling advocacy group member—yielded similar results: statistical tests point to weak instrumentation, but bike paths and lanes retain their significant and positive coefficient.

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Acknowledgments

This paper is based on a three-year research project funded by the U.S. Department of Transportation: “Analysis of Bicycling Trends and Policies in Large American Cities: Lessons for New York”. It is part of the Research Initiatives Program of the University Transportation Research Center, Region 2, for New York, New Jersey, Puerto Rico, and the Virgin Islands. The authors are indebted to Pat Mokhtarian, Bob Noland, Daniel Rodriguez, Dan Chatman, Radha Jagannathan, Kris Wernstedt, and Matt Dull for their help in revising the paper.

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Buehler, R., Pucher, J. Cycling to work in 90 large American cities: new evidence on the role of bike paths and lanes. Transportation 39, 409–432 (2012). https://doi.org/10.1007/s11116-011-9355-8

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Keywords

  • Bicycling
  • Urban transport
  • Infrastructure
  • Bike lanes
  • Bike paths
  • Sustainability