Six different strategies for updating split conformal predictive systems in an online (streaming) setting are evaluated. The updating strategies vary in the extent and frequency of retraining as well as in how training data is split into proper training and calibration sets. An empirical evaluation is presented, considering passenger booking data from a ferry company, which stretches over a number of years. The passenger volumes have changed drastically during 2020 due to COVID-19 and part of the evaluation is focusing on which updating strategies work best under such circumstances. Some strategies are observed to outperform others with respect to continuous ranked probability score and validity, highlighting the potential value of choosing a proper strategy.