Estimation of dynamic origin-destination demand for urban road networks incorporating a region-level traffic flow model

Abstract

Representative dynamic origin-destination (OD) demand is crucial for the assessment of network traffic performance. However, challenges for OD estimation include its underdetermination nature, data scarcity, and high computation cost. This paper proposes a mathematical optimization method for dynamic OD estimation (DODE) incorporating a link-level linear mapping method and a region-level traffic flow model. The region production and accumulation are estimated from loop detector data. It is considered an efficient method particularly for a large-scale network. A case study is conducted for a real-world network. After the optimization, we further test the estimated demand with microscopic traffic simulation which has not been calibrated. It is found that approximately 50% of the link count measurements already have GEH statistic values that are below 5. More examinations are required to validate the proposed method. The outcome can also be used as the initial solution in DODE using a simulation-based optimization approach.