A hybrid modelling framework for the estimation of dynamic origin–destination flows

Abstract

The dynamic origin–destination flow estimation (DODE) problem requires scalable methods for large scale traffic networks and consistent techniques for capturing both uncongested and congested traffic conditions. Despite numerous efforts on incorporating multifold data sources and developing manifold mathematical models, the DODE problem remains a challenging problem in terms of both scalability and consistency. To fill this gap, we propose a novel hybrid DODE framework that integrates region-level (macro) and centroid-level (micro) traffic dynamics. The region-level traffic flows are described by the macroscopic fundamental diagram, while the centroid-level traffic flows are represented by the linear mapping of origin–destination flows onto link counts. This hybrid approach enables us to (i) incorporate region-level traffic measures into the problem, addressing scalability issues arising in large scale traffic networks, and (ii) capture non-linear behaviour of traffic in the regional context, enhancing consistency of the estimation results with respect to traffic conditions. The proposed methodology is experimented in a large-scale traffic network, which is benchmarked for DODE problems. The results indicate an outstanding performance of the hybrid DODE particularly in congested traffic conditions and highlight the effectiveness of aggregated (regional) traffic models in enhancing DODE methods with minimal computational burden.