Spatially autocorrelated errors in origin-destination models: A new specification applied to aggregate mode choice

In this study, we use a first-order spatial autoregressive formulation to model the correlation among the errors of a linear demand equation that explains origin-destination flows. The process splits the error term for each observation into a weighted sum of all the other errors and a purely random noise. The weights are new parametric functional forms defined to measure the proximity between origins and destinations of flows. The parameters of these weights, along with the other parameters of the model, are estimated by the method of maximum likelihood. We apply the technique to an aggregate binary logit share model that explains peak A.M. trips to work in Winnipeg, Canada.