In this study we analyse the impact of congestion in dynamic origin-destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the relationship between OD flows and link flows is linearised. In this article the effect of using two types of linear relationship on the estimation process is analysed. It is shown that one type of linearisation implicitly assumes separability of the link flows, which can lead to biased results when dealing with congested networks. Advantages and disadvantages of adopting non-separable relationships are discussed. Another important source of error attributable to congestion dynamics is the presence of local minima in the objective function. It is illustrated that these local minima are the result of an incorrect interpretation of the information from the detectors. The theoretical findings are cast into a new methodology, which is successfully tested in a proof of concept.

In origin–destination (O-D) estimation methods, the relationship between the link flows and the O-D flows is typically approximated by a linear function described by the assignment matrix that corresponds with the current estimate of the O-D flows. However, this relationship implicitly assumes the link flows to be separable; this assumption leads to biased results in congested networks. The use of a different linear approximation of the relationship between O-D flows and link flows has been suggested to take into account link flows being nonseparable. However, deriving this relationship is cumbersome in terms of computation time. In this paper, the use of marginal computation (MaC) is proposed. MaC is a computationally efficient method that performs a perturbation analysis, with the use of kinematic wave theory principles, to derive this relationship. The use of MaC for dynamic O-D estimation was tested on a study network and on a real network. In both cases the proposed methodology performed better than traditional O-D estimation approaches, and thereby showed its merit.

Abstract-In this paper we propose a hierarchical approach for decomposing and simplifying the dynamic OD estimation procedure for large-scale congested networks. The main idea is to subdivide the network into multiple hierarchical levels. Next an OD estimation is performed on each level separately, starting with the highest level, and the output of this estimation is used as input for the OD estimation on a lower level. The main advantage of this approach is that different levels of complexity (of the DNL/DTA model and of the estimation method) can be used for different parts of the network as required. In addition, this hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods, which have been identified in previous research.

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