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Abstract:
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Highway crashes have recurring patterns with time, and may benefit from use of Time Series methodologies, and in specific, Seasonal Adjustment's predictive models to enhance highway safety and operation efforts for reducing crash fatalities/injuries. These crashes have patterns that repeat over fixed period of time. In some States the night-time, pedestrian, motorcycle, bicycle, rollover, fixed object, and winter crashes show weekly, monthly, or seasonal patterns. Contributing factors such as light condition, age group, weather, Season, economy, and others impact these variations. Modelling these patterns for highway safety performance, and removing the high-frequency seasonality will be a key to understanding the underlying dynamics for highway crashes. Current highway crash modelling practices ignore contributions of variables such as vehicle type, population, age, and income while data resources are available for correctly accounting for these variables. Scientific identification of major highway safety concerns will assist practitioners to identify effective safety countermeasures, and improve highway safety.
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