Publication at Faculty of Mathematics and Physics |
2013
Abstract
We made the passengers path planning more reliable by predicting irregularities in public transportation. The prediction is based on historical data, two levels of refinement are introduced and tested on sample data.
Applying prediction on actual timetables allows us to plan the path up to 19% more resilient against change loses. This can significantly help passengers to reach the destination in time.