Origin Destination Disaggregation Using Fratar Biproportional Least Squares Estimation for Truck Forecasting

This working paper describes a group of techniques for disaggregating origin-destination tables for truck forecasting that makes explicit use of observed traffic on a network. Six 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 found to work effectively. Introduction and Mathematical Underpinnings It is often desirable to obtain a highly detailed origin-destination table for vehicles or commodities, when only a much more aggregated table is available. These situations typically arise when survey data are organized into fairly large districts (zip codes, cities, counties or states) in order to preserve confidentially or simply to provide meaningful flow comparisons when the number of data samples is limited. Commercial vehicle and freight data, in particular, are prone to this type of spatial aggregation. For the purposes of this discussion, the aggregated OD table will be said to contain trip data between “districts”, while the disaggregated OD table will be said to contain trip data between “zones”. Traditional practice has been to disaggregate a district-level origin-destination table by factoring it along its rows and columns, simultaneously. That is: kl j i ij B A T τ = (1)