Forecasting Travel Patterns with Machine Learning: A Case Study Using Public Transport Data
This thesis investigates the application of machine learning and statistical methods for forecasting hourly public transport validations using ticket‐validation data. After cleaning and aggregating validations into hourly counts, temporal features such as hour of day, weekday, and month are engineered. The modeling approaches include long short‐term memory networks trained with mean absolute error
