Dynamic Origin-Destination Demand Estimation Using Turning Movement Counts

Abstract

A dynamic origin-destination (O-D) demand estimation model is presented that uses turning movement counts as observations. Based on an iterative bilevel estimation framework, the upper-level problem is to minimize a weighted objective function of the deviation between simulated link flows and real-time link counts and the deviation between estimated time-dependent demand and an a priori historical O-D table, where the weighting value is determined by an interactive approach to obtain the best compromise solution. A case study was performed on the US-29 network in Maryland to compare the estimated tables of this approach with the one obtained from the traditional method, which uses only approach link volume counts. The application illustrates considerable benefits of using turning movements instead of approach volumes in matching observed counts.