Strategic traffic management aims at the improvement of the functionality of the traffic network. This functionality is typically expressed in terms of the traffic flows and the travel times in the network. In this thesis a methodology for decision support in long-term traffic management is proposed. The core of the methodology is a traffic flow model in which it is assumed that the travelers choose their routes in accordance with the wardrop user equilibrium principle. The management goals, regarding traffic flows and travel times in the network, are presumed to be described by constraints. It is further presumed that a set of admissible actions in the network, to be used for achieving the goals, is specified. The proposed approach constitutes a systematic methodology for finding appropriate changes in the traffic network in order to fulfill the management goals.

The methodology includes two stages, and each stage amounts to solving a convex optimization problem. The problem in the first stage is a side constrained traffic equilibrium problem, for which a column generation procedure is proposed. This procedure is tested numerically on well known traffic equilibrium problem instances where link flow capacity constraints and general linear side constraints are added. The computational results are promising, especially for instances with a relatively small number of side constraints, which is likely to be the case in real-life applications.

The problem in the second stage is an inverse nonlinear multicommodity network flow problem, that is, the problem of finding a minimal cost link pricing scheme in a network, such that a given target link flow solution is optimal in a nonlinear multicommodity network flow problem. We present a solution algorithm for the inverse nonlinear multicommodity network flow problem. The algorithm is based on column generation. We present computational results for instances where the nonlinear multicommodity network flow problems are small and medium scale traffic equilibrium problems.

In extensive numerical illustrations it is shown how the decision support methodology can be applied to some typical traffic management scenarios. The ordering of the computations and the flexibility and the shortcomings of the procedure are exemplified.