= Solution
After adjusting for location, adding the period effect reduces deviance by $9.750$ on one degree of freedom, giving the supplied <likelihood-ratio test> $p\simeq0.002$. The adjusted <incidence rate ratio> is
$$
\boxed{e^{-0.7807}=0.4581,\qquad 95\%\text{ Wald interval }[e^{-0.7807-1.96(0.2754)},e^{-0.7807+1.96(0.2754)}]=[0.267,0.786].}
$$
Under this <Poisson exposure model>, the accident rate after installation is estimated to be about $54.2\%$ lower, with a statistically significant reduction. The remaining lack of fit merits residual checks and possibly a <Quasi-Poisson model> or a model allowing location-specific changes. This before-and-after observational study establishes an association under the fitted model; changes in traffic volume, broader trends, or <regression to the mean> at sites chosen for high prior counts could also contribute, so the estimated reduction is not by itself a causal treatment effect.
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