Nowadays we still live in a world of limited mobile data plans where a lot of apps make their use more comfortable and cost-efficient by being able to work offline. Unfortunately, usually it this also usually means a restriction in functionality. A commonly restricted feature is public transit route planning. The objective of my thesis is to create an offline route planner feature for a public transit schedule app which can plan routes door to door.
In my thesis I show how I filtered the data needed for route planning from a huge dataset. I and present the commonly used models for multimodal route planning with their pros and cons. I also give a brief overview of the relevant parts of the GTFS standard. Furthermore, I show the created app feature, the algorithm and the project responsible for the data filtering. Finally, after presenting the tests of the main quality attributes I evaluate my work and give an outlook to for future development.
The application was developed for Universal Windows Platform using the popular MVVM design pattern to make it easier to port the app to another .NET based framework.