Abstract
In developing statistical models of traffic accidents, flow, and roadway design, the R2 goodness-of-fit measure has been used for many years to (a) determine the overall quality and usability of the model, (b) select covariates for inclusion in the model, (c) make decisions as to whether it would be worthwhile to collect additional covariates, and (d) compare the relative quality of models developed from different studies. The pitfalls of using R2 to make these decisions and comparisons are demonstrated through computer simulations of commonly used accident prediction models, including the Poisson and negative binomial regression models. Because accident prediction models are nonnormal and functional forms are typically nonlinear, it is shown that R2 is not an appropriate measure to make any of the decisions and comparisons mentioned. Also, three properties are identified as desirable for any alternative measure to appropriately evaluate these models: (a) it should be bounded between 0 and 1—a value of 0 ...
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Schedule a callMore From: Transportation Research Record: Journal of the Transportation Research Board
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