Rodric Frederix

New Gradient Approximation Method for Dynamic Origin-Destination Matrix Estimation on Congested Networks

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.

Dynamic origin–destination estimation in congested networks: theoretical findings and implications in practice

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.

Dynamic Origin–Destination Matrix Estimation on Large-Scale Congested Networks Using a Hierarchical Decomposition Scheme

Despite its 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.

A density-based dynamic OD estimation method that reproduces within-day congestion dynamics

The OD estimation procedure is of paramount importance in traffic engineering problems. However, existing procedures are built upon the well-known static OD estimation methods, with the result that in practice the estimated OD flows sometimes hardly reproduce current traffic conditions. In this paper we identify the reasons that can cause this problem. Further, we propose a novel density-based OD estimation procedure that outperforms traditional dynamic estimation approaches, which tend to misinterpret the data, confusing free-flow and congestion regimes. The advantage of this approach is shown by performing demand estimation on the highway network around the city of Antwerp, Belgium.

The effect of dynamic network loading models on DTA-based OD estimation

Congestion causes substantial economic losses, both for individual and commercial transport. This chapter will examine the effect of using different queue mechanisms in the dynamic network loading (DNL) models on the dynamic origin destination (OD) estimation problem on different test networks. The chapter begins with a short review of OD estimation methods based on traffic counts, followed by a discussion of the different dynamic network loading models and the solution algorithm used in this chapter. Next, the effect of choosing one of the above mentioned queuing approaches is examined by solving the OD estimation problem on different test networks. In the last section of the chapter the authors formulate conclusions along with possible future research.