Objective. The main purpose of this study is to estimate and quantify the contribution of the infrastructure to highway crashes and to develop an infrastructure coefficient, that represents the overall characteristics of the highway and could be used as an independent variable in a crash-prediction model. Methods. The infrastructure is defined in this study as the highway and its geometric features, including alignment, road-side elements, sight-distances, presence of guardrails, access-points, roadway consistency, and additional variables that measure the overall quality of the highway alignment and elements. The analysis and developments are conducted for two-lane rural highways. The approach taken is to identify the high crash-rate roads, those with crash rates above 0.25 crashes per million vehicle-km, by Smallest Space Analysis. This type of analysis allows the aggregation of higher crash-rate roads versus lower-crash-rate roads only by their infrastructure coefficients, without consideration of their crash records. Results. Crash rates that are attached by Smallest Space Analysis to the group of roads that had less desirable infrastructure features show a high correlation between the same roads and high crash rates vs. identified better infrastructures and low crash rates. Further analysis shows that low crash-rate infrastructure, as defined in this study for two-lane rural highways, can reduce the crash rate by 44% versus high crash-rate infrastructure, at the 99% confidence level, which is almost a certainty. A model for the prediction of crash rates based on a proposed infrastructure coefficient is calibrated and presented. Conclusions. It is suggested that this model be used in evaluating alternatives for new highways or in improving the alignment and road features of existing highways.