Inferring an origin‐destination matrix directly from network flow sampling

Abstract

Motivated by the generalized‐inversion method, the authors offer a cost‐effective procedure to derive origin‐destination (O‐D) information from a readily available set of data: link traffic volumes, trip‐length frequency distributions, and “turning movements” at intersections. While developed independently, it can be related to the essential features of available models, including entropy, information and multiproportional theories. Empirical results from a large‐scale network in York, Pennsylvania — second largest data set reported in the literature — show that the algorithm is more accurate and it converges faster in comparison to similar studies.

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