Long-distance / International travel has seen little attention in the past, largely due to the irregular and sporadic nature of such trips. And yet, a single long-distance trip can amount to a distance equivalent to a year’s worth of commute trips, resulting in a similar, if not worse, environmental footprint. Understanding travellers’ behaviour is therefore just as relevant for such trips, as it is for everyday commute trips. As international travel is slowly picking up from the COVID-19 pandemic, it has been marred by an abundance of national and regional pandemic-related safety measures. While their primary goal is to protect the local population from infection, these safety may also make travellers feel safer while travelling. This perceived safety can differ from the true efficacy of the measures. In this research, we investigate people’s perception of eight COVID-19-related safety measures related to long-distance trips and how subjective perception of safety impacts their mode choice among car, train and aircraft. We employ a Hierarchical Information Integration (HII) approach to capture subjective perceptions and then model the obtained data by means of a Latent Class Choice Model, resulting in four distinct segments. To extrapolate the segments onto the rating experiment of HII, we apply a weighted least squares (WLS) regression, to obtain segment-specific safety perception. Two segments show a relatively high value-of-time (72€/h and 50€/h), tend to be more mode-agnostic and prefer determining the level of risk by themselves (relying primarily on infection and vaccination rate). The remaining two segments have a lower value-of-time (38€/h and 15€/h) and have strong mode affinity, for the train and car respectively. Future research could look into a way that segments the sample based on both the mode choice and rating experiment, providing additional insights into the heterogeneity of individuals in their perceptions.

This research aims to analyse the perception of covid-19 infection risk in long-distance travel in Europe and how it impacts mode choice and travel behaviour. We make use of an HII variant type experiment and model it by means of a latent class choice model, where we uncover four distinct user groups. For infection risk perception, we apply a novel approach in the field, utilising a weighted least squares regression, to obtain segment-specific regression functions, based on their respective probabilistic segment allocations. Some segments exhibit risk-aversion behaviour that is time-based (longer journeys perceived as more risky), whereas others see it as time-independent. With respect to modal preferences, the four segments either show a strong preference or aversion to one of the two land-based modes: car-loving, car-averse (using train or air), trainloving and train-averse (using car and air).