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A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies

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Abstract

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.

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Acknowledgements

We would like to thank Roya Amjadi, Wendy Martinez, Stas Kolenikov and the American Statistical Association’s Government Statistics Section for their encouragement. We would also like to thank Michael Sobel, Owen Ward and members of New York City Community Board 7, especially Richard Robbins and Catherine DeLazzero for their knowledge and expertise.

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Correspondence to Jonathan Auerbach.

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Auerbach, J., Eshleman, C. & Trangucci, R. A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies. Comput Stat 36, 1577–1604 (2021). https://doi.org/10.1007/s00180-021-01070-x

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