It is important to understand how public transport passengers value on-board crowding since the outbreak of the COVID-19 pandemic. The main contribution of this study is to derive the crowding valuation of public transport passengers in a post-pandemic era entirely based on observed, actual passenger route choices. We derive passengers’ crowding valuation for the London metro network based on a revealed preference discrete choice model using maximum likelihood estimation. We find that after the passenger load on-board the metro reaches the seat capacity, the in-vehicle time valuation increases by 0.42 for each increase in the average number of standing passengers per square metre upon boarding. When comparing this result to a variety of crowding valuation studies conducted before the pandemic in London and elsewhere, we can conclude that public transport passengers value crowding more negatively since the pandemic. Furthermore, we found a ratio between out-of-vehicle time and in-vehicle time of 1.94 pre-pandemic and of 1.92 post-pandemic, based on which we conclude that the relative waiting/walking time valuation did not significantly change since the COVID-19 pandemic. Our study results contribute to a better understanding on how on-board crowding in urban public transport is perceived in a European context since the outbreak of the COVID-19 pandemic.@en

Understanding the passenger demand impacts of public transport service changes is a fundamental aspect of transport planning. The main objective of this study is to derive an updated Generalised Journey Time (GJT) elasticity for urban and metropolitan public transport networks, by applying a revealed preference approach using individual passenger journey data. Based on more than 25 million empirical journeys subject to 9 different service interventions within the Greater London area, we find an average GJT elasticity of −0.61. The value implies that for every 1% increase in generalised journey time, on average public transport demand is expected to reduce by 0.61%, and vice versa. We also find that the demand response to service changes is most elastic during the midday period between the peak hours and most inelastic during the AM peak and early morning, possibly caused by a higher share of mandatory journeys. Our study results confirm the existence of a build-up rate from the initial short-run elasticity to a somewhat stronger longer-run elasticity. Besides, we find that at least within the short- and medium-term demand is more elastic to service degradations compared to service improvements. Our findings imply that it requires more time for demand to increase in response to a service quality improvement, compared to demand to decrease after a service quality reduction.@en

Understanding how passengers perceive public transport interchanges is important to better explain current public transport mode and route choice behaviour and to better predict future demand levels. In this study we derive how passengers value a public transport interchange in a metropolitan context entirely based on recent, large-scale, Revealed Preference data, explicitly distinguishing between different types and modes of public transport interchanges. For this purpose we estimate three discrete choice models using maximum likelihood estimation, based on over 26,000 passenger route choices observed in June 2023 in the Greater London Area. We find that each public transport interchange is on average valued equivalent to 5 min uncrowded in-vehicle time. Additionally, our model results provide quantitative evidence that cross-platform interchanges between two metro journey legs are valued 20–25 % less negatively than a regular metro interchange where a level change is required. Multimodal bus-metro interchanges and out-of-station interchanges are perceived most negatively by passengers. Passengers value bus-bus interchanges on average about 60 % more negatively than metro-metro interchanges, possibly driven by factors such as comfort, service frequency, reliability and (perceived) safety. Our study results can be used for business case and appraisal purposes, when quantifying the impact of service changes which affect the number or type of interchanges.@en