Horizontal curve advisory speeds often vary more than they should. To understand why, it helps to shift attention from the roadside to the method behind the sign. For decades, many agencies have relied on ball-bank or inclinometer-based approaches. On paper, the method is straightforward: drive the curve, observe the lateral response, and infer a comfortable advisory speed. That familiarity is exactly why the method endured for so long and evolved from analogue to digital instrumentation. Yet the research record is full of warnings about its limitations, regardless of the sophistication of the recording method.
Bonneson et al. concluded that the ball-bank indicator creates real obstacles to achieving advisory speeds that are uniform and credible.
The problem is not that the tool is old, analogue, or digital. It is that the result can be shaped by the driver, the run, the vehicle, the speed profile, road imperfections, and the interpretation of what counts as a satisfactory pass.
That subjectivity shows up in the field. Leaming’s 2014 work for Oregon Department of Transportation (ODOT) is useful because it does not simply criticize traditional practice; it quantifies the practical challenge of getting stable answers.
That is a revealing statement. It means the job is not only to pick a number, but to pick one that can survive scrutiny despite the noise built into the process.
And that is the turning point in the wider narrative. Once a method depends on repeated field judgement, consistency becomes hard to scale across a network. Dixon and Rohani made this explicit when they compared approaches in Oregon and highlighted the computational alternative.
That does not make computation magical; it simply changes the nature of the work. The credibility of the final sign depends on whether the underlying procedure can deliver results that are repeatable, explainable, and defensible at scale. If the method cannot do that, consistency will always remain more aspiration than reality.
The next part of the series turns from method to implication: what consistency makes possible, and why that matters for safety outcomes.