Local information on crashes, traffic volumes and roadway geometry is essential for SPF calibration because

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Multiple Choice

Local information on crashes, traffic volumes and roadway geometry is essential for SPF calibration because

Explanation:
Local calibration of the Safety Performance Function must reflect the actual conditions of the roads you’re evaluating. This means using crash history, traffic volumes (exposure), and roadway geometry because these factors determine how often and what types of crashes occur on a given network. Local crash patterns can differ due to design features, road class, curvature, sight distance, lane width, grades, and other geometric characteristics, as well as typical driving behavior in that area. By incorporating local data, the SPF’s predictions align with what’s observed on similar roads nearby, making the model more accurate for planning and evaluating countermeasures. Relying on national data can hide these local differences, and relying on weather data alone ignores exposure and the influence of road design on crash risk. Ignoring local geometric features would also weaken the model’s ability to predict crashes tied to specific road designs.

Local calibration of the Safety Performance Function must reflect the actual conditions of the roads you’re evaluating. This means using crash history, traffic volumes (exposure), and roadway geometry because these factors determine how often and what types of crashes occur on a given network. Local crash patterns can differ due to design features, road class, curvature, sight distance, lane width, grades, and other geometric characteristics, as well as typical driving behavior in that area. By incorporating local data, the SPF’s predictions align with what’s observed on similar roads nearby, making the model more accurate for planning and evaluating countermeasures.

Relying on national data can hide these local differences, and relying on weather data alone ignores exposure and the influence of road design on crash risk. Ignoring local geometric features would also weaken the model’s ability to predict crashes tied to specific road designs.

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