Fractals are irregular geometric objects that exhibit finite details at all scales, and when magnified, their basic structures remain the same regardless of the scale of magnification. The main strength of fractal analysis is in its ability to find patterns in large and complex datasets. To benefit from fractal theory, a dataset must exhibit fractal characteristics. This paper investigates the existence of fractal characteristics in crash data from Florida using the fractal dimension and the Hurst exponent techniques. Using crash data from 1991-2011, it was found that the annual crash rate trend at signalized intersections exhibited fractal characteristics. Results of the annual crash rate analysis could be applied in the identification of high-crash intersections. For example, fractal analysis using the Hurst exponent technique could be used to predict crash trend and whether a high-crash intersection would continue to be on future high-crash location lists.
