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

Abstract

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.