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To select appropriate traffic safety measures in curved roadways, we focus on the structure of drivers’ cognitions of lead-vehicle speeds in curves. The purpose of this study is to propose an accident-preventive measure selection method based on the speed- cognition structure of the lead-vehicle driver in a curved roadway. In order to test the hypothetical structure of the speed cognitions, we use path-analysis approach and employ a driving simulator. And in our hypotheses about the speed-cognition structure and the curve cognition process of the driver, we focus on the relationship among a target-setting error due to the maximum safety speed (TSE), a subjective-adjustment error due to perceived speed (SAE), an “objective-adjustment error due to perceived speed (OAE) a speed-perception error due to actual speed (SPE), and a maximum-safety error due to actual speed (MSE)”. In the results of our driving-simulator experiment, in the case of left-hand curves, the measures that have drivers perceive the perceived speed low during passing through the curve are effective for diminishing the actual vehicle speed. In the case of right-hand curves, on the other hand, the measures that have drivers target the target speed low before proceeding into the curve are effective for diminishing the actual vehicle speed. In the class of sharp curves, drastic measures such as improvements to the working of the curve are better than measures such as the impact on the perceived speed. In the class of gentle curves, on the other hand, even though in the same class, the measures should be handled sensitively before proceeding into the curve or while passing through the curve, depending on the degree of the change of curvature

Accident-preventive Measure Selection Method based on the Speed Cognition of Lead-vehicle Driver in Curved Roadway