New gradient approximation method for dynamic origin-destination matrix estimation on congested networks

In origin-destination (OD) estimation methods the relationship between the link flows and OD flows is typically approximated by a linear function described by the assignment matrix corresponding with the current estimate of the OD flows. However, this relationship implicitly assumes the link flows to be separable, which leads to biased results in congested networks. We suggest the use of a different linear approximation of the relationship between OD flows and link flows that takes into account that link flows are non-separable. However, deriving this relationship is cumbersome in terms of computation time. In the present paper, we propose to use Marginal Computation (MaC), a computationally efficient method that performs a perturbation analysis using Kinematic Wave Theory principles, to derive this relationship. The use of MaC for dynamic OD estimation is tested on a study network and on a real network. In both cases the proposed methodology performs better than traditional OD estimation approaches, indicating its merit.status: publishe