Origin–destination table disaggregation using biproportional least squares estimation

This paper describes a group of techniques for disaggregating origin–destination tables for travel forecasting that makes explicit use of observed traffic on a network. Five models within the group are presented, each of which uses nonlinear least-squares estimation to obtain row and column factors for splitting trip totals from and to larger geographical areas into smaller ones. The techniques are philosophically similar to Fratar factoring, although the solution method is quite different. The techniques are tested on a full-sized network for Northfield, MN and are found to work effectively.