Traditional methods for constructing simulation models can involve severalsteps that require manual pre-processing of large data sets. This process maybe time-consuming and prone to human errors, while also leading to modelsthat are inconvenient to customize for varying simulation scenarios. In thisthesis, we propose an alternate data preparation methodology in emergencyvehicle transport simulation, which aims to eliminate parts of the manualpre-processing. Our research is based on a previous case study using datafrom Sweden’s Southern Healthcare Region. The methodology we propose isinstantiated through a proof-of-concept software module that replacespreviously used static input sets by introducing dynamic runtime calculationsof ambulance travel times. This was done in two steps where we first evaluatedseveral routing engines according to needs extracted from the studied case.Secondly, we implemented and integrated the chosen routing engine into thepreviously mentioned module. Testing of the module showed feasible andconsistent performance, demonstrating the potential usage of our proposedmethodology in emergency vehicle transport simulation.