Parameter optimization methods for estimating dynamic origin-destination trip-tables

Dynamic origin-destination tables help in on-line control of traffic facilities and, consequently, are of significant use in alleviating traffic congestion. Such tables find useful applications in the contexts of Advanced Traffic Management Systems and Advanced Traveler Information Systems. This paper considers the estimation of split parameters that prescribe an origin-destination trip-table based on dynamic information regarding entering and exiting traffic volumes through an intersection or a small freeway segment. Two models are developed and motivated for this problem, one based on a least-squares estimation approach and the other based on a least absolute norm approach. Both models enhance existing dynamic origin-destination trip-table estimation models in that they also consider freeway segments having differing time-dependent transfer lags between different pairs of entrances and exits. A projected conjugate gradient scheme is employed for solving the constrained least-squares problem and is compared against a standard commercial software. The least absolute norm estimation problem is posed as a linear programming problem and is also solved using a commercial software for the sake of comparison. Computational results are presented on a set of test problems using synthetic as well as realistic simulated data, involving the determination of origin-destination trip tables for both intersection and freeway scenarios, in order to demonstrate the viability of the proposed methods. These results exhibit that, unlike as reported in the literature based on previous efforts, properly designed parameter optimization methods can indeed provide accurate estimates in a real-time implementation framework. Hence, these methods provide competitive alternatives to the iterative statistical techniques that have been heretofore used because of their real-time processing capabilities, despite their inherent inaccuracies.