  Long-distance travel has seen little attention in the past, largely due its
sporadic nature. 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 thus
just as relevant for such 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 - and likely
does - 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 (72EUR/h and 50EUR/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 (38EUR
/ h and 15EUR/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.
