Ridepooling represents a flexible and sustainable mobility solution that complements traditional public transport. Especially during periods of fluctuating demand induced by external events such as concerts, festivals, sporting events, trade fairs, strikes, and severe weather conditions, it provides a reliable and efficient alternative for attendees and commuters. This study investigates the impact of external events on the demand for MOIA's ridepooling service in Hamburg. It utilizes ridepooling trip request data from January 2023 to March 2024, together with event data, to quantify the changes in ridepooling demand in response to such external events and understand the variability in demand among different user groups.

The study employs a methodology that includes exploratory data analysis, statistical testing, and regression modeling. Initial exploratory analysis reveals visual patterns in demand corresponding to event occurrences. Statistical tests identify specific events that are significantly associated with changes in demand. Generalized linear regression models are developed to quantify the impact of these events on overall daily demand and demand per user group.

Findings indicate that ridepooling demand is highly sensitive to certain external events while showing no effect from others. Additionally, the demand for the same event varies significantly across different user groups. All events that were found to be significant in impacting ridepooling demand show an increase in demand, except trade fairs, which present a slight decrease. Public transport strikes in metro and bus operations show the most pronounced impact. The baseline daily demand increases up to sixfold, depending on the user group. However, for the Heavy-User group, this kind of event is found to be insignificant. Other events, like football games, outdoor and indoor events, show slight demand increases of 2\% to 11\% for most groups, while temperature and precipitation show no significant effect for any group.

These results can help ridepooling operators optimize their service and improve municipal transport planning. Also, this research adds to the scientific literature by offering a deeper understanding of external events and overall transportation demand through an analysis of the previously unexplored transportation mode, ridepooling.