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.

动态起点-终点流量估计（DODE）问题需要针对大规模交通网络的可扩展方法以及捕获非拥塞和拥堵交通状况的一致技术。尽管在整合多重数据源和开发多种数学模型方面做出了许多努力，但 DODE 问题在可扩展性和一致性方面仍然是一个具有挑战性的问题。为了填补这一空白，我们提出了一种新颖的混合 DODE 框架，该框架集成了区域级（宏观）和质心级（微观）流量动态。区域级流量由宏观基本图描述，而质心级流量由起点-目的地流到链路计数的线性映射表示。这种混合方法使我们能够（i）将区域级流量测量纳入问题中，解决大规模交通网络中出现的可扩展性问题，以及（ii）捕获区域环境中流量的非线性行为，增强估计的一致性与交通状况有关的结果。所提出的方法在大规模交通网络中进行了实验，并以 DODE 问题为基准。结果表明，混合 DODE 具有出色的性能，特别是在拥堵的交通条件下，并强调了聚合（区域）交通模型在以最小的计算负担增强 DODE 方法方面的有效性。提高交通状况估计结果的一致性。所提出的方法在大规模交通网络中进行了实验，并以 DODE 问题为基准。结果表明，混合 DODE 具有出色的性能，特别是在拥堵的交通条件下，并强调了聚合（区域）交通模型在以最小的计算负担增强 DODE 方法方面的有效性。提高交通状况估计结果的一致性。所提出的方法在大规模交通网络中进行了实验，并以 DODE 问题为基准。结果表明，混合 DODE 具有出色的性能，特别是在拥堵的交通条件下，并强调了聚合（区域）交通模型在以最小的计算负担增强 DODE 方法方面的有效性。