References of "Frederix, Rodric"
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See detailDynamic Origin-Destination Matrix Estimation on Large-Scale Congested Networks Using A Hierarchical Decomposition Scheme
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Journal of Intelligent Transportation Systems (2014), 18(1), 51-66

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires ... [more ▼]

Despite the ever increasing computing power, dynamic Origin-Destination (OD) estimation in congested networks remains troublesome. In previous research, we have shown that an unbiased estimation requires the calculation of the sensitivity of the link flows to all Origin Destination flows, in order to incorporate the effects of congestion spillback. This is however computationally infeasible for large-scale networks. To overcome this issue, we propose a hierarchical approach for off-line application that decomposes the dynamic OD estimation procedure in space. The main idea is to perform a more accurate dynamic OD estimation only on subareas where there is congestion spillback. The output of this estimation is then used as input for the OD estimation on the whole network. This hierarchical approach solves many practical and theoretical limitations of traditional OD estimation methods. The main advantage is that different OD estimation method can be used for different parts of the network as necessary. This allows applying more advanced and accurate, but more time consuming methods only where necessary. The hierarchical approach 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. [less ▲]

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See detailImproving the efficiency of repeated dynamic network loading through marginal simulation
Corthout, Ruben; Himpe, Willem; Viti, Francesco UL et al

in Transportation Research. Part C : Emerging Technologies (2014), 41

Currently, the applicability of macroscopic Dynamic Network Loading (DNL) models for large-scale problems such as network-wide traffic management, reliability and vulnerability studies, network design ... [more ▼]

Currently, the applicability of macroscopic Dynamic Network Loading (DNL) models for large-scale problems such as network-wide traffic management, reliability and vulnerability studies, network design, traffic flow optimization and dynamic origin–destination (OD) estimation is computationally problematic. The main reason is that these applications require a large number of DNL runs to be performed. Marginal DNL simulation, introduced in this paper, exploits the fact that the successive simulations often exhibit a large overlap. Through marginal simulation, repeated DNL simulations can be performed much faster by approximating each simulation as a variation to a base scenario. Thus, repetition of identical calculations is largely avoided. The marginal DNL algorithm that is presented, the Marginal Computation (MaC) algorithm, is based on first order kinematic wave theory. Hence, it realistically captures congestion dynamics. MaC can simulate both demand and supply variations, making it useful for a wide range of DNL applications. Case studies on different types of networks are presented to illustrate its performance. [less ▲]

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See detailDynamic OD estimation in congested networks: theoretical findings and implications in practice
Frederix, Rodric; Viti, Francesco UL; Tampere, Chris M.J.

in Transportmetrica (2013), 9(6), 494-513

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 ... [more ▼]

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. [less ▲]

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