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Sensitivity Analysis on Traffic Crash Prediction Models by Using STATA

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  • Deo Chimba

    (Graduate Research Assistant, Department of Civil Engineering, Florida State University.)

Abstract

Traffic accidents results from the interaction of different parameters which includes highway geometrics, traffic characteristics and human factors. Geometric variables include number of lanes, lane width, median width, shoulder width, roadway length, number of intersections, access density and shoulder width while traffic characteristics include AADT and speed. The effect of these parameters can be correlated by predictive models that predict crash rates at particular roadway section. STATA software commands it can be used to test the sensitivity of these variables on crash rate after modeling. In the current research sponsored by Florida Department of Transportation titled "Evaluation of Geometric and Operational Characteristics affecting the safety of Six-lane divided Roadways" we use these commands to determine the effect in crash rate as the result of change on these independent variables. We selected our model based on the user written command nbvargr which gives dispersion factor between Poisson and Negative Binomial. By using Vuong's value we were able to choose between Zero-Inflated and normal models. Through the command listcoef, percent we determine percent change in crash rate for unit and standard deviation increase in independent variables. By using the command mfx compute we were able to determine numerically the marginal effects or the elasticities between crash rate and the independent variables. These commands and others built in STATA reveal if the increase in size or dimension for roadway geometrics will result in higher crash rate or reduction.

Suggested Citation

  • Deo Chimba, 2004. "Sensitivity Analysis on Traffic Crash Prediction Models by Using STATA," North American Stata Users' Group Meetings 2004 3, Stata Users Group.
    • Handle: RePEc:boc:asug04:3
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