Predictable motorway ramp curves are safer

Motorway safety depends largely on curve geometry and driver behaviour, a relationship that has implications for research and practice. This paper introduces a novel approach to quantifying geometric design consistency, defined as the degree to which drivers’ expectations of curve radii match actual road geometries. The hypothesis is that if a driver expects a larger curve than that actually present, an accident might occur because of an excessively high approach speed. To test this hypothesis, this study uses Dutch motorway data, including ramp and curve characteristics, as well as crash frequencies. The data were employed in three steps: 1) constructing a Bayesian model that mimics drivers’ expectations, 2) testing the predictions of this model against real curve characteristics, and 3) examining the relationship between disparities in expectations, reality, and crash frequency. The results indicated a positive correlation between disparities in expectations, reality, and crash frequency. This finding suggests that the crash frequency is higher when drivers expect a larger curve than what is present. The Tree Augmented Naïve Bayesian Network (TAN) reveals the complexity of curve expectations, demonstrating that drivers anticipate larger radii in connector ramps and higher speeds with gentler curve angles. Applying this research to motorway design involves using TAN predictions and crash frequency models to assess safety in motorway curve design, which could proactively improve road safety.