Modelling changes in public transport demand amid disruptive events

Disruptive events, such as natural disasters, social movements or pandemics, can severely impact public transport demand. The relevance of studying the impacts of these events on public transport has been widely recognised in the literature, focusing on the investigation of disruption mitigation, delay management, vulnerability and resilience. Much less attention, however, has been given to understanding passengers’ responses amid these events. As a consequence, several gaps in this research area are yet to be addressed. These include a narrow scope in the modelling of passengers’ behavioural responses amid disruptive events, the lack of understanding of the role of individual-level factors in those responses (e.g. socio-demographics, attitudes and trip characteristics) and an insufficient examination of passive data sources for aggregate and disaggregate-level analysis (ranging from smart card to more emerging data sources such as aggregate mobility indices). This motivates this research, whose aim is to enhance the understanding of public transport demand during disruptive events. The research conducted here is temporally framed between 2019 and 2022, a period of worldwide high mobility disturbances caused by the COVID-19 pandemic. This period represents a unique opportunity to address existing research gaps in the analysis of disruptive events and public transport demand by leveraging recent literature and data sources. It is demonstrated in this research that passengers’ adaptations are more complex than the current ‘trip reduction’ approach adopted in the literature. In this regard, this thesis adds to the body of existing knowledge by identifying and modelling passengers’ mobility profiles and departure time choices during disruptive events. This research also supports using passive data sources such as smart card data and emerging aggregated mobility indices to analyse public transport demand change amid disruptive events. In particular, by addressing some of the existing challenges of these data sources, their potential to be employed for a broader range of events has been revealed in this research. This research also highlights the role of passengers’ associated characteristics on distinctive behavioural responses adopted during disruptive events, recognising a strong presence of inequality. These findings suggest that, regarding disruptive events, public transport agencies and operators should especially focus on the needs of the more vulnerable population segments, who showed fewer opportunities to mitigate the impacts of disruptive events through travel behavioural adaptation. Finally, the findings generated in this thesis can be used to improve the understanding of how passengers adapt their mobility patterns during external disruptions and, therefore, be used by policymakers to act accordingly amid future disruptive events.