Combined model calibration and spatial aggregation

Abstract

A methodology is proposed for estimating a combined transportation model that accommodates spatial aggregation. It employs maximum likelihood estimation using a joint probability function that includes destination and mode choice simultaneously. The main contribution of the paper is the incorporation of a spatial aggregation strategy to validate the method when the survey data are insufficient. By aggregating small zones into larger districts, estimation of the trip distribution parameters can be achieved with limited data, while mode choice continues to be estimated using disaggregated data. Our results demonstrate that including trip distribution in the travel decision induces variations in the utility function parameter estimators obtained for the different travel modes when only mode choice is estimated. The methodology thus corrects omission bias.