Abstract (EN):
The strategic analysis and planning of investments on transportation infrastructure is something that
inherently has a long term horizon and, as such, should be carefully studied and thought through. This
PhD project addresses the problem of how to best optimize the investments made on a transportation
network in order to minimize the transportation costs and environmental impacts of freight
transportation, and to maximize the transportation network robustness. The long term nature of this
kind of analysis means that some factors and parameters that today assume a certain value, may change
quite significantly in the long run. Due to that, the best possible investment scenario will likely vary
according to the values of the parameters used, which means that the developed model has to be able
to accommodate for various key parameter values, and multiple future scenarios.
The goal of this PhD project is to develop a strategic freight transportation network optimization model
that weights in several different factors and is flexible enough to accommodate for possible changes in
key factors, so that it can be used as an important tool in planning investments for new and improved
transportation infrastructures.
After having completed the needed literature review step, the following objectives are expected to be
accomplished under this PhD project: The first objective is to develop a strategic freight transportation
traffic assignment model, capable of assigning freight traffic to a network, in which the freight
movements (O/D - Origin/Destination - tables) and the transportation costs for each type of movement
are introduced by the user. Due to its macro nature, the model will not consider congestion, but will
take capacity limits into account and assume a stochastic component in modal choice. The second
objective is to conceive a freight network optimization model, which, based on the developed freight
transportation model, and using factors such as costs and environmental impact to evaluate each
scenario, is able to find an optimal (or close to optimal) way in which to invest a specific volume of
capital on a transportation network. The third objective is to apply the network optimization model to
several artificial networks in an effort to understand if there is a pattern in the way transportation
investments should be made. To finalize, the forth objective is to perform a case study, by applying the
proposed model to a real world network and analyzing the results.
Language:
English
Type (Professor's evaluation):
Scientific