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

Jonathan Auerbach (), Christopher Eshleman and Rob Trangucci
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Jonathan Auerbach: Columbia University
Christopher Eshleman: The Port Authority of New York & New Jersey
Rob Trangucci: University of Michigan

Computational Statistics, 2021, vol. 36, issue 3, No 3, 1577-1604

Abstract: 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.

Keywords: Hierarchical Bayes; Before–after study; Vision Zero; Selection bias (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-021-01070-x

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