Using Non-Parametric Tests to Evaluate Traffic Forecasting Performance.

This paper proposes the use of a number of nonparametric comparison methods for evaluating traffic flow forecasting techniques. The advantage to these methods is that they are free of any distributional assumptions and can be legitimately used on small datasets. To demonstrate the applicability of these tests, a number of models for the forecasting of traffic flows are developed. The one-step-ahead forecasts produced are then assessed using nonparametric methods. Consideration is given as to whether a method is universally good or good at reproducing a particular aspect of the original series. That choice will be dictated, to a degree, by the user’s purpose for assessing traffic flow.