Title: A Hierarchical Bayes Approach to Adjust for Selection Bias in Before-After Analyses of Vision Zero Policies,
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.