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The COVID-19 pandemic drastically changed the way of living for billions of people and severe restrictions were implemented by governments around the world, affecting the travel patterns of all citizens. This article investigates how travel patterns changed in the Greater Copenhagen area of Denmark during the full two-year period covering 2020 and 2021, thus allowing for an analysis of both the short-term and medium-term impacts as society gradually reopened and restrictions were lifted. The analysis covers large-scale travel survey data as well as a segmentation clustering analysis of public transport smart card data. The results showed that impacts were strongly linked to changes in trip purpose and were thus not uniformly distributed throughout the public transport system. User segmentation analysis revealed that most users changed to less intense travel use of public transport. The results highlight important policy implications in terms of how to adapt service provision within a public transport network more efficiently.

In a crowded metro network, it is not unusual to observe that passengers actively choose not to board but wait for the next train for a seat, even if there is vacant standing room on the arriving train. We analyse such behaviour using a logit-based choice model based on revealed preference data collected from the smart card records and the operational timetables. The choice model considers waiting time, fluctuating crowding levels, and passengers’ expected seat availability at each station on their trip. The revealed preference data are collected based on an existing time component framework, which can estimate passengers’ itineraries by dividing passengers’ travel time into time components (i.e. access, egress, boarding delay, and transfer-walking times) and analysing their uncertainty. We improve the time component framework by developing methods for estimating distributions corresponding to each time component. Using Chengdu Metro as a case, we find that the extra waiting time resulting from active boarding delay and standing time is valued 50.5% more positively and 25.3% more negatively than the in-vehicle sitting time, respectively. By comparing our findings with studies focused on passive boarding delays caused by fully loaded trains, we suggest that extra waiting time due to active and passive boarding delays should be explicitly distinguished in practice. The estimation results of the distributions for the time components indicate that access and egress walking times follow different distributions at given stations, as opposed to the assumption in most prior studies.