Does Accuracy Really Matter? Isn't 60–70% Good Enough?
It's a fair question. In fact, it's common enough that I think it's worth answering properly.
Let's start with an analogy.
Imagine you're buying a watch. You have two options. One costs a little more upfront but keeps perfect time, day after day. The other is cheaper, but it occasionally loses a few minutes. If you're only wearing it as a fashion accessory, maybe that's fine. But if you rely on it to get to work on time, pick up your kids, catch a flight, or make appointments, suddenly those "small" inaccuracies become expensive.
The cheaper watch wasn't actually cheaper—it just hid its costs until later.
People flow management systems are much the same. Yet surprisingly often, airports still choose the equivalent of the cheap watch.
Why? Upfront cost is certainly part of the answer. Sometimes it's also because a new technology is more flashy or promises lower prices or broader coverage. But let's ignore every other consideration—software, installation, usability, coverage—and focus on just one thing: accuracy.
At first glance, 60–70% accuracy might sound perfectly acceptable. After all, it's still right more often than it's wrong. The problem is that accuracy isn't consistent.
A system advertised as 70% accurate doesn't perform at exactly 70% under every condition. In reality, accuracy changes with passenger density, queue layouts, staff interventions, and passenger behavior. One hour it might achieve 90% accuracy. The next, it could fall to 40%.
And that's where the real problem begins.
If you don't know when the system is accurate—and when it isn't—how much confidence can you place in any of the data?
Personally, I'd argue that in many situations, having no data is actually better than having misleading data. At least when you know you don't have the answer, you can make decisions accordingly. Bad data, on the other hand, gives you false confidence.
What if the system is only used for passenger information?
If the goal is simply to give passengers a rough idea of waiting times, then perhaps extreme accuracy isn't essential. An airport could display broad estimates, like 5–20 minutes or 10–30 minutes
Those estimates might even be based on experience rather than automated measurement. But are they actually useful?
For a passenger deciding whether to grab a coffee, head straight to security, or make a flight connection, a range of 15 or 20 minutes isn't particularly informative. If airports want to display meaningful waiting times—for example, in 5-minute intervals—they need a system capable of producing reliable and consistent measurements. Otherwise, precise-looking numbers simply create a false sense of certainty.
Accuracy becomes critical when money is involved
Passenger information is only one use case. Many airports also use queue measurement systems to monitor Service Level Agreements (SLAs) with security providers, airlines, or ground handling companies.
These contracts often specify targets such as:
95% of passengers must wait less than five minutes.
Maximum queue lengths must not be exceeded.
Service levels must be maintained throughout operating hours.
In some airports, payments or penalties are directly linked to these measurements.
Now imagine those calculations are based on a system that's only 70% accurate. Suddenly the consequences become much more serious. An airport could overpay or underpay a contractor. A security provider might dispute the results. Operational decisions could be based on performance data that simply isn't correct.
Nobody would willingly accept unreliable measurements when contracts and money are involved. That’s a stakeholder alignment issue, a “blame game” that has its own cost—which can be very high!
Average accuracy hides the real problem
There's another issue that's often overlooked. A system with lower overall accuracy isn't simply "a little less accurate." Its performance is also far less predictable.
Accuracy fluctuates depending on factors such as:
passenger density
queue configuration
staff interventions
passenger behavior
That instability makes long-term trend analysis difficult and reduces confidence in every KPI the system produces.
Ultimately, the question isn't whether a system is 70% accurate. The question is whether you can trust the data when making operational decisions.
Accuracy is really about trust
Queue measurement systems aren't installed simply to count people. They're installed to help airports make better decisions. Those decisions include:
managing passenger flow
allocating staff
monitoring service levels
planning future infrastructure
demonstrating contractual compliance
All of those depend on one thing: trustworthy data. Accuracy, therefore, isn't just another specification on a product datasheet. It's the foundation of operational trust.
A reliable system gives airport operators confidence to act. An unreliable one creates uncertainty—and in an airport environment, uncertainty is often far more expensive than the difference in purchase price.
Tags: | passenger flow management | airport operations | waiting time monitoring | queue analytics | airport technology |
