Moral rhetoric in discrete choice models: a Natural Language Processing approach.

This paper proposes a new method to combine choice- and text data to infer moral motivations from people's actions. To do this, we rely on moral rhetoric, in other words, extracting moral values from verbal expressions with Natural Language Processing techniques. We use moral rhetoric based on a well-established moral, psychological theory called Moral Foundations Theory. We use moral rhetoric as input in Discrete Choice Models to gain insights into moral behaviour based on people's words and actions. We test our method in a case study of voting and party defection in the European Parliament. Our results indicate that moral rhetoric have significant explanatory power in modelling voting behaviour. We interpret the results in the light of political science literature and propose ways for future investigations.

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

With a few exceptions, public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are now likely to adapt their behaviour with a focus on factors that contribute to the risk of COVID-19 transmission. Given the unprecedented spatial and temporal scale of this crisis, these changes in behaviour 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 at the end of the first infection wave. 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 latent traveller segments: 'COVID Conscious' and 'Infection Indifferent'. The former has a significantly higher valuation of crowding, accepting, on average 8.75 min extra waiting time (average total travel time in the choice scenarios was about 40 min) to reduce one person on-board. Moreover, this class indicates a strong desire to sit without anybody in the neighbouring seat and is quite sensitive to changes in the prevalent infection rate. By contrast, the Infection Indifferent class has a value of crowding (1.04 waiting time minutes/person) that is only slightly higher than pre-pandemic estimates and is 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. We believe that 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.

The consumer-citizen duality: Ten reasons why citizens prefer safety and drivers desire speed.

Cost-benefit analyses for transportation projects usually value impacts on safety and travel time through experiments in which consumers of mobility ('drivers') choose between routes which differ in safety and travel time. This approach has been criticized for failing to consider that private choices may not fully reflect citizens' preferences over public goods and means, a concept known as the consumer-citizen duality. Recent empirical evidence has established that individuals do indeed assign comparatively more value to safety in their role as citizens than in their role as drivers. Our study aims to provide explanations for this finding by presenting four stated choice experiments in which respondents were asked to make choices, both as citizens and as drivers, between routes that differed in travel time and safety. Subsequently, respondents were asked to provide reasons for their choices. We identify five cognitive and five normative explanations. The cognitive explanations suggest that individuals make diverging choices because their perceptions of accident risk differ between the two roles. Drivers will assign a relatively low value to mitigating accident risk because they believe that: (1) such risks are trivial on an individual level; (2) their personal risk is lower than the average risk; (3) their personal risk is controllable; (4) they would not be able to distinguish relative safety levels in real life; and (5) their choices for others are more risk-averse than choices for themselves and, unlike citizens, they are not explicitly evaluating risky choices for others. The normative explanations involve that individuals believe that the government should assign more value to safety compared to individual drivers because: (6) as citizen they are more prone to base their choices on social norms which prescribe risk-averse behaviour in this context; (7) governments have a duty of care concerning the safety of the transportation network; (8) drivers have a relatively high degree of responsibility to reduce their own travel times; (9) governments should account for drivers' tendencies to choose faster routes by building safer ones; and (10) governments should ensure the safety of the road network because this allows drivers to choose the fastest route without being concerned about the impact of their route choice on accident risk.