Consistent origin-destination and link flow estimation based on data-driven network assignment

Origin-Destination (OD) and link flows are crucial input to several traffic planning and management problems. In this paper we evaluate a data-driven network assignment method that first estimates the assignment process using GPS probe data, then performs a simultaneous and consistent OD and link flow estimation. The method is evaluated on empirical data for Stockholm, Sweden, showing that the method explains a significant part of the variance in link flow observations for both a training and test set of link flow observations. The results also show the importance of calibrating both the weight parameters in the OD estimation step and the Logit parameter in the route choice model. Using a too small weight on the a priori OD matrix will lead to large variance and over-fitting of the estimated OD against link counts, while a too large weight will cause unnecessary bias in the results.