122 research outputs found
A new Spring, a new sound
Special editorial from the outgoing and incoming Editor in Chief
COVID-19 risk-perception in long-distance travel
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
An adaptive route choice model for integrated fixed and flexible transit systems
Over the past decade, there has been a surge of interest in the transport
community in the application of agent-based simulation models to evaluate
flexible transit solutions characterized by different degrees of short-term
flexibility in routing and scheduling. A central modeling decision in the
development of an agent-based simulation model for the evaluation of flexible
transit is how one chooses to represent the mode- and route-choices of
travelers. The real-time adaptive behavior of travelers is intuitively
important to model in the presence of a flexible transit service, where the
routing and scheduling of vehicles is highly dependent on supply-demand
dynamics at a closer to real-time temporal resolution. We propose a
utility-based transit route-choice model with representation of within-day
adaptive travel behavior and between-day learning where station-based
fixed-transit, flexible-transit, and active-mode alternatives may be
dynamically combined in a single path. To enable experimentation, this
route-choice model is implemented within an agent-based dynamic public transit
simulation framework. Model properties are first explored in a choice between
fixed- and flexible-transit modes for a toy network. The framework is then
applied to illustrate level-of-service trade-offs and analyze traveler mode
choices within a mixed fixed- and flexible transit system in a case study based
on a real-life branched transit service in Stockholm, Sweden.Comment: 33 pages, 9 figures, preprin
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