Public transportation systems operate in dynamic and unpredictable environments, necessitating close monitoring of real-world operations. Integrating data into route design, scheduling, and policy formulation is essential for creating an efficient and sustainable transit network. This thesis utilises historical bus travel time data to predict future bus travel times, enhancing the analysis of network reachability.

A literature review identified four prediction models to be investigated: Historical Average, Vector AutoRegression, Random Forest, and Long Short-Term Memory deep neural network. A case study involving six bus lines in Groningen was formulated, providing the necessary KoppelVlak 6 and GTFS schedule datasets to be used as inputs for the prediction models. During model development, patterns in historical travel time data were identified, and prediction accuracy was evaluated. The output from the most accurate prediction model was then utilised as input for the reachability analysis.

The analysis demonstrated that complex machine learning models, such as Random Forest and Long Short-Term Memory deep neural networks, yielded the most accurate predictions. The time of day when the journey occurs is particularly predictive of travel and dwell times. Integrating these predictions into a reachability analysis revealed instances of increased reachability, decreased reachability, and missed transfers compared to the original schedule.

The findings demonstrate that travel time prediction models can significantly enhance reachability analyses. This more accurate representation of reachability can be used to identify systemic issues in the design of the public transportation network, allowing for interventions to improve performance.

Current geopolitical tensions mean that NATO countries must now make greater efforts to deter threats to their territories, having neglected this defence task for years. In case of a major conflict, brigades and their vehicles are ordered to immediately travel to the area of conflict. This vehicle deployment is researched and modelled in a Multi-Integer Linear Programme (MILP), with the objective to minimise the makespan of the deployment, e.g. the arrival time of the last convoy. In this deployment the strategic and operational movements are modelled, meaning the starting point is the Point of Embarkation (POE) and the end point is the Staging Area (SA) or Concentration Area (CA). The model is suited for deployments that use a double modal network, being road and rail transport. Two scenarios are established, distinguishable by their transport networks and vehicle types, and they are subjected to configurations in which parameters are varied. The research is conducted in cooperation with the Royal Netherlands Army (RNLA), and serves as a handle to obtain understanding on deployments when varying certain parameters. It is observed that the usage of the rail mode mainly should be motivated by vehicle suitability, rather than makespan oriented reasoning, as it hardly improves the makespan of the deployment. Furthermore, platooning is a promising technology to implement in the deployment, with the ability to decrease the makespan by at least 11%. The influence of traffic congestion halfway the deployment is highlighted, encompassing an increase in makespan of at least 7-15%. It is found that the convoy amount and the amount of vehicles does not influence the makespan by a lot (pm1.9%). Lastly, the difference between the transport networks of the two scenarios is exposed with the use of a robustness examination, where the makespan of the first scenario improves substantially more (11.7%) than the makespan of the second scenario (0.4%). This research can be expanded in several directions, including the incorporation of additional modalities and a more comprehensive investigation into the robustness of transport networks within these deployments.