We investigate whether American cities can expect to achieve a meaningful reduction in pedestrian deaths by lowering the posted speed limit. We present our work in three sections. First we briefly motivate the problem and provide a description of the dataset. Second we fit a log-linear model and compare sources of variation with an analysis of variance. Finally we demonstrate a sample use case. We evaluate the decision to lower many of New York City's posted speed limits from 30 mph to 25 mph. In our evaluation we assume the assignment of speed limits to roads is ignorable given measured covariates, and we calculate the number of lives saved by estimating the causal effect of lowering the speed limit on New York City roads from 30 mph to 25 mph on 25 mph roads. We find some evidence that a lower speed limit does in fact reduce fatality rates, and our estimated causal effect is similar to the traditional before-after analysis espoused by policy analysts. Nevertheless, we conclude that adjusting the posted speed limit in urban environments does not correspond with a reliable reduction in pedestrian fatalities.

Code and data available at the stancon_talks GitHub repository (https://github.com/stan-dev/stancon_talks)