Statistical Inference for Gravity Models in Transportation Flow Forecasting (1997)

Abstract:

Gravity models are a class of log-linear regressions that have been used in studies of traffic flows between geographical zones. Stochastic parameter variations on these models, and their Bayesian analyses via stochastic simulation, are explored here in connection with the development of approaches to studying variability questions in es­tablished traffic flow network equilibrium models. In addition to developing methods of statistical inference and prediction specific to gravity models, this paper provides discus­sion of general concepts of Bayesian modelling and stochastic simulation analysis that will be of wider interest to the transportation community.