A novel approach for estimation of dynamic from static origin–destination matrices

Abstract

The growing interest in deploying advanced technologies to enhance the efficiency of transportation systems has generated a greater need for traffic simulation software to test and validate these systems prior to their field implementation. The precise estimation of dynamic origin–destination (O–D) matrices is critical to the successful application of these models. In addressing this need, the paper develops a novel algorithm that estimates the dynamic O–D matrix by first computing static O–D matrices for each time slice and then adjusting these matrices to account for the temporal dependency across the various time slices. Specifically, a simulation-based package integrating the QueensOD and INTEGRATION software is developed to estimate the dynamic O–D matrix. First, the time-dependent static O–D matrices are estimated using the QueensOD maximum likelihood O–D estimator. Next, these static matrices are applied as a seed solution to estimate the dynamic O–D demand by modeling the detailed movement of vehicles using the INTEGRATION microscopic traffic assignment and simulation software. The study demonstrates that the dynamic O–D matrix estimated from the proposed package produces a significant reduction in the link flow error compared to the assignment of the time-varying static O–D matrices. The study also demonstrates that by increasing the update frequency, the dynamic O–D matrix becomes closer to the actual O–D demand when compared to the static matrices.

Keywords:

• Dynamic O–D estimation
• optimization
• synthetic O–D estimation
• INTEGRATION software
• QueensOD software

Notes

No potential conflict of interest was reported by the authors.

1 Here, we apply relative errors to find the optimum O–D matrix.

Additional information

Funding

This research effort was funded by the Connected Vehicle Initiative University Transportation Center (CVI-UTC).