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.

美国城市投入了大量资源来实施防止行人死亡的交通安全对策。然而，通常用于评估这些对策是否成功的前后比较通常会受到选择偏差的影响。本文以纽约市的零愿景战略为例，激发了选择偏差的倾向，以高估交通安全政策的好处。NASS 一般估计系统、死亡率分析报告系统和其他数据库组合成贝叶斯分层模型，以计算更真实的前后比较。结果证实，纽约市零愿景政策的前后分析确实高估了该政策的效果，而更现实的估计是大约三分之二的规模。