A new class of dynamic methods for the identification of origin-destination flows

A new systems dynamics approach for the identification of origin-destination (O-D) flows in a traffic system is presented. It is the basic idea of this approach that traffic flow through a facility is treated as a dynamic process in which the sequences of short-time exit flow counts depend by causal relationships upon the time-variable sequences of entrance flow volumes. In that way enough information can be obtained from the counts at the entrances and the exits to obtain unique and bias-free estimates for the unknown O-D flows without further a priori information. Four different methods were developed: an ordinary least squares estimator involving cross-correlation matrices, a constrained optimization method, a simple recursive estimation formula and estimation by Kalman filtering. The methods need only moderate computational effort and are particularly useful for tracking time-variable O-D patterns for on-line identification and control purposes. An analysis of the accuracy of the estimates and a discussion of the convergence properties of the methods are given. Finally, a comparison with some conventional static estimation procedures is carried out using synthetic as well as real data from several intersections. These tests demonstrated that the presented dynamic methods are highly superior to conventional techniques and produce more accurate results.