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Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands

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arxiv 2104.10973 v2 pith:7ORHZEJZ submitted 2021-04-22 econ.GN q-fin.EC

classification econ.GNq-fin.EC
keywords infectionpandemicbehaviourtravellerspublictransportchangescovid-19
verification ladder T0 review T1 audit T2 compute T3 formal
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Public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are likely to adapt their behaviour to avoid the risk of transmission and these changes may even be sustained after the pandemic. To evaluate travellers' behaviour in public transport networks during these times and assess how they will respond to future changes in the pandemic, we conduct a stated choice experiment with train travellers in the Netherlands. We specifically assess behaviour related to three criteria affecting the risk of COVID-19 transmission: (i) crowding, (ii) exposure duration, and (iii) prevalent infection rate. Observed choices are analysed using a latent class choice model which reveals two, nearly equally sized traveller segments: 'COVID Conscious' and 'Infection Indifferent'. The former has a significantly higher valuation of crowding, accepting, on average 8.75 minutes extra waiting time to reduce one person on-board. Moreover, they demonstrate a strong desire to sit without anybody in their neighbouring seat and are quite sensitive to changes in the prevalent infection rate. By contrast, Infection Indifferent travellers' value of crowding (1.04 waiting time minutes/person) is only slightly higher than pre-pandemic estimates and they are relatively unaffected by infection rates. We find that older and female travellers are more likely to be COVD Conscious while those reporting to use the trains more frequently during the pandemic tend to be Infection Indifferent. Further analysis also reveals differences between the two segments in attitudes towards the pandemic and self-reported rule-following behaviour. The behavioural insights from this study will not only contribute to better demand forecasting for service planning but will also inform public transport policy decisions aimed at curbing the shift to private modes.

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