The papers in this special issue on transportation network design were first presented at a workshop held at the University of Illinois in September 1977 under the auspices of the Mathematical Social Science Board with the support of the National Science Foundation. The participants in the...

Two versions of an optimal network design problem with shipments proportional to transportation costs are formulated. Extensions of an algorithm developed in prior research for solving these problems are proposed and tested. The performance of the algorithms is found to improve substantially as...

In this note, a simple network equilibrium based approach for estimating a trip matrix using link traffic count data is proposed. In essence a combined distribution and assignment model is formulated in which the link flow data serve to furnish an estimate for the sum of the integrals of the...

Three network design problems are formulated for optimizing discrete additions to link capacities. Trip distribution and assignment are functions of travel expenditure in each problem. A reformulation of Wilson's doubly-constrained trip distribution problem, in which entropy instead of total...

This research studies heterarchical collaboration in logistical transport. Specifically, it utilizes a hybrid Delphi-Analytic Hierarchy Process (AHP) approach to explore the relevant criteria for the formation and maintenance of a strategic alignment for heterarchical transport collaboration....

In this report, we compare the computational efficiency and results of solving two alternative models for the problem of determining improvements to an urban road network. Using a 1462 link, 584 node test network of the north Dallas area, we compare a model which assumes user-optimum behavior of...

It is known that the network design problem with the assumption of user optimal flows can be modeled as a 0-1 mixed integer programming problem. Instead, we formulate the network design problem with continuous investment variables subject to equilibrium assignment as a nonlinear optimization...