Time-Dependent Origin-Destination Estimation Without Assignment Matrices

Abstract

Time-dependent origin-destination (OD) flows are crucial inputs to dynamic traffic assignment (DTA) models. However, they are often unobserved, and must be estimated from indirect traffic measurements collected from the study network. Approaches to estimate OD flows from link counts traditionally rely on assignment matrices that map the OD flow variables onto the counts. However, this method (a) approximates the complex relationship between OD flows and counts with a linear function, (b) is restricted to the use of only counts, and cannot exploit richer data such as speeds, densities or travel times, and (c) cannot estimate route choice and supply parameters that critically impact the OD estimates. We present a dynamic OD estimation method that is accurate, flexible in the use of general traffic data, simultaneously estimates all parameters that impact OD estimation, and can be applied to any traffic assignment model.