Why Speed‑Camera Studies Cannot Prove What They Claim

In other words…

Disingenuous Studies Generate Dishonest Findings

As with any study that makes exciting claims, it is reasonable to examine the methodology used to reach the study’s conclusions. When we do that, some interesting common denominators emerge.

When these studies are examined collectively, a structural problem emerges. Despite their technical polish, they all share a fundamental limitation: none can isolate the camera itself as the causal agent of the observed changes. And it is universal across the very studies these papers cite.

The NYC study acknowledges that speeding behavior changed during COVID‑19, that cameras were installed continuously throughout the study period, and that traffic volumes were unavailable for crash‑rate calculations. It also concedes that “the crash analysis lacked detailed traffic volume data due to data unavailability,” meaning the study cannot compute exposure‑adjusted crash risk. These are not minor footnotes—they are fatal constraints.

The same limitations appear in the foundational studies the NYC paper cites. De Pauw et al. (2014a, 2014b) examined Belgian camera sites using before‑after comparisons, but these locations were selected precisely because they had abnormal crash histories. Regression to the mean—where unusually high crash counts naturally fall in subsequent years—cannot be removed statistically, even with control groups. Graham et al. (2019) used Bayesian causal inference across 771 UK sites, but their “reference sites” were not equivalent in terms of geometry, traffic, or enforcement. Carnis & Blais (2013) used interrupted time‑series methods in France, but the national rollout of cameras coincided with sweeping road‑safety campaigns, new signage, and speed‑limit changes. Tang (2017) used a difference-in-differences analysis in Great Britain, but DID cannot control for unobserved differences between treated and untreated roads.

The NYC study itself demonstrates the problem. It reports a “statistically significant positive correlation” between the number of cameras and the number of tickets issued. This means ticket volume is driven not by driver behavior but by the number of installed automated ticket cameras. When the number of cameras increases, it is axiomatic that the number of recorded violations would also increase—even if actual speeding does not. Clearly, ticket counts are a moot method for measuring driver behavior and an unreliable proxy for safety.

The crash analysis faces an even deeper issue. The study uses a survival analysis model (SARE) to estimate a 14% reduction in crashes. But without exposure data—such as vehicle miles traveled, pedestrian volumes, or traffic composition—crash counts alone cannot establish risk reduction. A road with half the traffic will naturally have fewer crashes, regardless of enforcement. The authors acknowledge that COVID‑19 dramatically altered traffic patterns, yet the model treats the pandemic as a simple binary “interruption.”

See: https://doi.org/10.1016/j.aap.2013.03.003

Across all cited studies, the same confounding factors recur:

• Non‑random camera placement at high‑crash or politically sensitive sites
• Simultaneous interventions such as road diets, signage changes, and police blitzes
• Seasonal and economic shifts in traffic volume
• Pandemic‑induced behavioral anomalies
• Sparse crash data, which produces high variance and unstable estimates
• Lack of true control groups, because no two roads are trully comparable

Let’s look at each of these factors:

When cameras are installed in response to spikes, crashes will almost always fall afterward—even if nothing was done. When traffic volumes collapse during a pandemic, crash counts will fall regardless of enforcement. When new cameras are added, and ticket counts predictably rise, that is more likely evidence of unchanged driver behavior and, therefore, no improvement in overall safety.

The result is a body of literature that consistently reports reductions but cannot demonstrate causation. The NYC study’s authors themselves note that “other confounding factors such as changes in traffic volume… may also have an impact” and that future research must incorporate these missing variables.

In short, the studies cited in support of automated enforcement do not—and cannot—prove that cameras themselves improve safety. They measure correlations in environments where causation cannot be isolated. Policymakers deserve honest clarity: the evidence base for speed‑camera safety claims is far weaker than the headlines suggest.

We can’t say categorically whether these studies reveal flaws in the researchers’ integrity. We do, however, find it interesting that when the government sponsors “research” ostensibly aimed at improving safety, its methodology is often flawed, and its solutions tend to be unproven.

Meanwhile, it is the general public who end up paying in the end. With every camera installed, our freedoms have been incrementally reduced without our consent. And we are the ones who pay our hard-earned money, in part, to the private companies that administer these programs and, in larger part, to the cities whose far-from-impartial courts are willing participants in the freedom-reducing money-grab.

It is a situation that will persist as long as the people affected by it allow it to persist.