Dynamic origin-destination matrix estimation for networks operating under free-flow conditions using macroscopic flow dynamics

The origin-destination (OD) matrix is a crucial requirement for efficient transportation planning, however its estimation still remains one of the most challenging tasks in transportation. This paper proposes a novel methodology for the estimation of dynamic OD matrices. The proposed methodology integrates disaggregated measurements from stationary sensors, e.g. loop detectors, with a macroscopic model that associates OD demands with Traffic counts on specific network links. We investigate networks that operate under free-flow conditions, hence the dynamic OD matrix estimation problem is formulated as a linear mathematical program. Finally, we reformulate the problem to obtain a computationally cheap quadratic programming problem that allows real-time estimation of OD matrices within the time-window under study. We demonstrate the effectiveness of our methodology through a literature network, showcasing its ability to accurately capture travel patterns in a computationally efficient manner, allowing informed decision-making processes in transportation planning and management.