A heuristic bilevel programming for the origin-destination demand adjustment problem

초록

In this paper, we propose an efficient heuristic algorithm for the origindestination matrix adjustment problem (DAP). DAP is usually formulated as a bilevel optimization problem where the upper level problem is a generalized least squares problem with nonnegativity constraints and the lower level problem is formulated as a fixed demand traffic assignment problem. In this paper, we apply a Gauss-Seidel type projected gradient approach combined with the column generation for the lower level problem and for upper level, we solve a generalized least squares problem. The proposed algorithm provides explicit partial derivative for upper level objective function, and converges fast using the second derivative information. Using Sioux Falls network, Winnipeg network, and a large scale network having 1,859 nodes, 4,776 links, and 15,582 OD pairs, we demonstrate our algorithm. © 2015 ICIC International.

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