Statistical models for the estimatio of the origin-destination matrix from traffic counts

Abstract

In transportation planning, one of the first steps is to estimate the travel demand. The final product of the estimation process is an origin-destination (OD) matrix, whose entries correspond to the number of trips between pairs of origin-des na on zones in a study region. In this paper, we review the main sta s cal models proposed in the literature for the es ma on of the OD matrix based on traffic counts. Unlike reconstruc on models, sta s cal models do not aim at es ma ng the exact OD matrix corresponding to observed traffic volumes, but they rather aim at es ma ng the parameters of a sta s cal model of the popula on of OD matrices. Ini ally we define the es ma on problem, emphasizing its underspecified nature, which has lead to the development of several models based on different approaches. We describe sta c models whose parameters are es mated by means of maximum likelihood, the method of moments, and Bayesian inference. We also describe some recent dynamic models. Following that, we discuss research ques ons related to the underspecifica on problem, model assump ons and the es ma on of the route choice matrix, and indicate promising research direc ons.