PARAMETER OPTIMIZATION METHODS FOR ESTIMATING DYNAMIC ORIGIN-DESTINATION TRIP-TABLES

Dynamic origin-destination tables help in on-line control of traffic f acilities and, consequently, are of significant use in alleviating tra ffic congestion. Such tables find useful applications in the contexts of Advanced Traffic Management Systems and Advanced Traveler Informati on Systems. This paper considers the estimation of split parameters th at prescribe an origin-destination trip-table based on dynamic informa tion regarding entering and exiting traffic volumes through an interse ction or a small freeway segment. Two models are developed and motivat ed for this problem, one based on a least-squares estimation approach and the other based on a least absolute norm approach. Both models enh ance existing dynamic origin-destination trip-table estimation models in that they also consider freeway segments having differing time-depe ndent transfer lags between different pairs of entrances and exits. A projected conjugate gradient scheme is employed for solving the constr ained least-squares problem and is compared against a standard commerc ial software. The least absolute norm estimation problem is posed as a linear programming problem and is also solved using a commercial soft ware for the sake of comparison. Computational results are presented o n a set of test problems using synthetic as well as realistic simulate d data, involving the determination of origin-destination trip tables for both intersection and freeway scenarios, in order to demonstrate t he viability of the proposed methods. These results exhibit that, unli ke as reported in the literature based on previous efforts, properly d esigned parameter optimization methods can indeed provide accurate est imates in a real-time implementation framework. Hence, these methods p rovide competitive alternatives to the iterative statistical technique s that have been heretofore used because of their real-time processing capabilities, despite their inherent inaccuracies. (C) 1997 Elsevier Science Ltd.