Looking briefly, they're all before-after correlational studies (longitudinal). These are not as good as randomized experiments, but they're still much better than a cross-sectional correlation (eg. "we looked at all traffic lights; ones with cameras have higher accident rates p=0.xyz").

For example, given a cross-sectional correlation result like that, there's a very easy retort: "people only install cameras at dangerous intersections!" The longitudinal design deals with that: "but they weren't so dangerous before the cameras were installed!"

Now a critic must look to less likely explanations: "maybe there has been a traffic-crime wave whose early phases caused both the installation and later increased traffic rates" (or something like that, I don't know much about the issue). It is to deal with all these more exotic variants that one wants to step up a level and add randomization.