Efforts to manage truck flows in congested urban areas have important implications not only for congestion relief, but also for air quality improvement and reductions in energy use. A vital input to these flow management efforts is knowledge of the origin-destination (O-D) movement patterns for various classes of trucks, and this presents a substantial challenge. The problem of estimating O-D tables from observed data (usually link counts) has been studied since the 1970’s, and there are effective methods for a single-class (i.e., passenger cars) problem under assumptions of deterministic user equilibrium flow patterns in the network. There has also been some work on extending the ideas to conditions reflecting stochastic user equilibrium.

Relatively little work has been done on the multi-class problem, which is of particular concern for estimating truck flows in different size classes. Furthermore, as more advanced traffic surveillance technologies become available, data beyond simple link counts can be used in the estimation process. Finally, the transition from estimating a static O-D pattern to estimating dynamic O-D flows creates additional challenges.

The purpose of this project is to test a newly developed method for estimating multi-class O-D tables for trucks, using more comprehensive observable data than link counts, and reflecting the uncertainty inherent in network flows by using a stochastic equilibrium formulation, rather than a deterministic one. The model formulation uses a bi-level optimization, for which a specialized solution method has been developed. In this project, the solution method will be tested using a set of networks of varying character and data of varying types. The result of this project will be a validated method for improving the estimation of multi-class truck origin-destination flows, based on a fusion of data from various sources and reflecting the uncertainty in network flow patterns. This is a key step in moving toward the ability to better manage truck flows in real time to reduce congestion, reduce energy consumption, and improve air quality in urban areas.