Access control techniques are used to improve traffic performance and safety on highways. One important benefit of access control is improved safety. For a quantitative assessment of the benefits of access control on safety, impact models are needed to predict crash frequencies based on the geometric and access control characteristics of the segments. The objective of this research was to develop regression models to predict crash frequencies on urban multilane arterial segments. To develop these models, data were collected on geometric and access control characteristics of the segments and the number of crashes on the segments by severity type. Negative binomial regression models were developed to predict the total number of crashes, number of property-damage-only crashes, and number of fatal and injury crashes. The three models have a similar structure. The exposure-to-risk variables include segment length, number of years, and annual average daily traffic. The significant factors include density of access points, proportion of signalized access points, presence of an outside shoulder, presence of a two-way left-turn lane, and presence of a median with no openings between signals. The results indicate that access control has a beneficial effect on safety.
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