Multiclass Probit-Based Origin–Destination Estimation Using Multiple Data Types

Abstract

This paper proposes a bilevel optimization model for multiclass origin–destination (O–D) estimation using various types of data. The multiclass character of the model, a new feature and major contribution to the literature, is important because of increasing interest in simultaneous estimation of O–D tables for various classes of trucks and automobiles. The upper-level optimization is used to derive O–D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm and offer important insights into the multiclass O–D estimation process with the different types of available data. Adding turning movement data can improve O–D estimation by 71%. Furthermore, classification information is interchangeable among different types of sensors.