Improving O-D flows updating through aggregation of O-D pairs: Methodological formulation and performance analysis of a heuristic algorithm

Performance of origin-destination (o-d) flows updating using link counts is highly influenced by the unbalance between the information provided by link counts and the number of o-d flows to update. In this respect, the paper proposes an approach to update o-d flows, based on their aggregation through a sequential algorithm, whose inputs are a prior o-d flows estimate, a set of counted links, and a prior estimate of the assignment matrix. The approach aggregates sequentially suitable pairs of o-d flows with a stepwise selection based on two criteria, namely coverage and proximity. Resulting aggregated o-d flows are then updated by means of a classical Generalized Least Squares (GLS) estimator. Finally, resulting updated o-d flows are disaggregated back to the original granularity using o-d flow proportions coming from the prior o-d flows estimate, thus introducing a bias. Notwithstanding, experimental results on a real-size network show the proposed approach to improve significantly o-d flows updating. Rules-of-thumb for the application of the proposed approach are also discussed, with operational and interesting implications for researchers and practitioners.