On the statistical analysis of non‐homogeneous traffic streams

The statistical analysis of counting processes obtained from extensive sets of traffic data is often complicated by the presence of long‐term fluctuations in the rate of traffic flow. This paper describes a technique which corrects for the effects of a non‐homogeneous rate function by transforming the observed process to a time scale in which the traffic rate is approximately constant. The transformation is based on properties of the non‐homogeneous Poisson process, but it is useful in estimation and testing of a much broader class of point process models. In determining the appropriate transformation for a sequence of aircraft arrivals, both kernel estimators and exponential polynomial models for the rate function are employed. The effect of the transformation on the estimated coefficient of variation for the headway distribution and on tests for serial dependence is demonstrated. The transformation allows stationary parametric models to be constructed for traffic indices in periods of fluctuating flow r...