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Disturbances in the railway network are frequent and to some extent, inevitable. When this happens, the traffic dispatchers need to re-schedule the train traffic and there is a need for decision support in this process. One purpose of such a decision support system would be to visualize the relevant, alternative re-scheduling solutions and benchmark them based on a set of relevant train traffic attributes which quantify the effects of each solution. Currently, there are two research projects financed by the Swedish Transport Administration (i.e. Trafikverket) which focus on developing decision support to assist the Swedish train traffic managers: The STEG project and the EOT project. Within the STEG project, researchers at Uppsala University in co-operation with Trafikverket are developing a graphical user interface (referred to as the STEG graph). Within the EOT project, researchers at Blekinge Institute of Technology (BTH) are developing fast re-scheduling algorithms to propose to the Swedish train traffic dispatchers a set of relevant re-scheduling alternatives when disturbances occur. However, neither the STEG graph nor the EOT algorithms are at this point designed to evaluate, benchmark and visualize the alternative re-scheduling solutions. The main objective of this work is therefore to identify and analyze different train traffic attributes and how to use the selected relevant ones for benchmarking re-scheduling solutions. This involves enhancing an existing visual tool (EOT GUI) and using this extended version (referred to as the EOT GUI+) to demonstrate and evaluate the benchmarking of different re-scheduling solutions based on the selected train traffic attributes. The train traffic attributes found in the literature (foremost research publications and documents by Trafikverket) were collected and analyzed. A subset of the most commonly used attributes found were then selected and their applicability in benchmarking re-scheduling solutions for the Swedish train traffic system was further analyzed. The formulas for calculating each of the attribute values were either found in the literature and possibly modified, or defined within this thesis project. In order to assess the use of the attributes for benchmark solutions, experiments were conducted using the enhanced visual tool EOT GUI+ and a set of sample solutions for three different disturbance scenarios provided by the EOT project. The tool only performs a benchmark of two solutions at a time (i.e. a pair wise benchmark) and computes the attribute values for the chosen attributes. The literature review and attribute analysis resulted in a first set of ten different attributes to use including e.g. total final delay (with a delay threshold value of 1 and 5 minutes respectively), maximum delay, total accumulated delay, total delay cost, number of delayed trains and robustness. The formulas to compute these attribute values were implemented and applied to the sample solutions in the experiments. The first phase of the experiments showed that in one of the disturbance scenarios, some of the attribute values were in conflict and that none of re-scheduling solution was dominating the others. This observation led to that the set of attributes needed to be narrowed down and internally prioritized. Based on the experimental results and the analysis of what the research community and the main stakeholder (i.e. Trafikverket) consider are the most important attributes in this context, the final set of attributes to use includes average final delay, maximum delay of a single train, total number of delayed trains and robustness. The contribution of this thesis is primarily the review and analysis of what attributes to use when performing a benchmark of re-scheduling solutions in real-time train traffic disturbance management. Furthermore, this thesis also contributes by performing an experimental assessment of how the attributes and their formulas could work in a pair-wise, quantitative benchmark for a set of disturbance scenarios and which issues that may occur due to conflicting objectives and attribute values. Concerning the enhancement of the visual tool and the visualization of the re-scheduling solutions, the experimental evaluation and analysis shows that the tool would not fit directly to the needs of the train dispatchers. This work should therefore only be seen as a starting point for the researchers whom are working with the development of decision support systems in this context. Furthermore, several iterative experiments have been conducted to select the appropriate attributes for benchmarking solutions and suggesting the best re-scheduling solution. During the experiments, we have used a limited set of different problem instances (2+2+7) representing three different types of disturbances. The performance of the enhanced visual tool EOT GUI+ and its functionalities should ideally also be analyzed further and improved by experimenting with a larger number of instances, for other parts of the Swedish railway network and in co-operation with the real users, i.e. the dispatchers.