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...

Choice modelling in the age of machine learning - Discussion paper

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems...

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...

Estimating decision rule differences between ‘best’ and ‘worst’ choices in a sequential best worst discrete choice experiment

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining...

An artificial neural network based method to uncover the value-of-travel-time distribution

This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate...

On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preference survey

Many experts believe the transport system is about to change dramatically. This change is due to so-called fully-automated vehicles (AVs). However, at present, there are numerous important knowledge gaps that need to be solved for the successful integration of AVs in our transport systems, in particular regarding the impacts of AVs on travel...

Using Artificial Neural Networks for Recovering the Value-of-Travel-Time Distribution

The Value-of-Travel-Time (VTT) expresses travel time gains into monetary benefits. In the field of transport, this measure plays a decisive role in the Cost-Benefit Analyses of transport policies and infrastructure projects as well as in travel demand modelling. Traditionally, theory-driven discrete choice models are used to infer the VTT...

Does the decision rule matter for large-scale transport models?

This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive)...

Accounting for variation in choice set size in Random Regret Minimization models

This paper derives a trick to account for variation in choice set size in Random Regret Minimization (RRM) models. In many choice situations the choice set size varies across choice observations. As in RRM models regret level differences increase with increasing choice set size, not accounting for variation in choice set size results in RRM...