Path-Based Origin-Destination Matrix Estimation Utilizing Macroscopic Traffic Dynamics

The origin-destination (OD) matrix is a crucial requirement for transportation management and planning. Efficient OD matrix estimation is important to enhance the advancement of intelligent transportation systems. We present a novel approach for the estimation of static OD matrices using within-day traffic flow dynamics. The signalised cell transmission model (CTM) is utilised to capture the dynamics of a specific network and associate road segment count observations with path demands. This model is extended to capture per-path densities, yielding a path-based OD matrix problem formulation that results in a nonlinear optimisation problem. Efficient solution methodologies, based on convex and nonconvex optimisation theory, are developed for free-flow and congested conditions, respectively. In contrast with the majority of research for the OD matrix estimation problem, this work offers the following advantages: 1) no prior or target OD matrices are needed to implement the approach outlasting the bias and dependency on such matrices, 2) no historical data are required for accurate estimations, 3) no route choice model or split ratios are needed, 4) no user equilibrium conditions are required for high-quality estimation, and 5) even low partial coverage of the network is sufficient to provide high-quality OD matrix estimation. We illustrate the efficiency of the proposed approach on three literature real-life arterial networks and show that the proposed approach yields accurate results under both free-flow and congested scenarios.