Freeway Origin Destination Matrices: Not as Simple as They Seem

Abstract

Travel demand can be elegantly represented using an Origin-Destination (OD) matrix. The link counts observed on the network are produced by the underlying travel demand. One could use these counts to reconstruct the OD matrix. An offline approach to estimate a static OD matrix over the peak period for freeway sections using these counts is proposed in this research. Almost all the offline methods use linear models to approximate the relationship between the on-ramp and off-ramp counts. Previous work indicates that the use of a traffic flow model embedded in a search routine performs better than these linear models. In this research that approach is enhanced using a microscopic traffic simulator, AIMSUN, and a gradient based optimization routine, MINOS, interfaced to estimate an OD matrix. This approach is an application of the Prediction Error Minimization (PEM) method. The problem is non-linear and non-smooth, and the optimization routine finds multiple local minima, but cannot guarantee a global minima. However, with a number of starting seed matrices, an OD matrix with a good fit in terms of reproducing traffic counts can be estimated. The dominance of the mainline counts in the OD estimation and an identifiability issue is indicated from the experiments. The quality of the estimates improves as the specification error, introduced due to the discrepancy between the traffic flow model and the real world process that generates the on-ramp and off-ramp counts, reduces.