Exploring the potential adoption of Mobility-as-a-Service in Beijing: A spatial agent-based model
Transportation Research Part A Policy and Practice,
Год журнала:
2025,
Номер
194, С. 104430 - 104430
Опубликована: Март 1, 2025
Язык: Английский
Assessment of the barriers in establishing passenger mobility-as-a-service (MaaS) systems: An analogy with multimodal freight transport
Case Studies on Transport Policy,
Год журнала:
2025,
Номер
unknown, С. 101433 - 101433
Опубликована: Март 1, 2025
Язык: Английский
Emerging Research Issues and Directions on MaaS, Sustainability and Shared Mobility in Smart Cities with Multi-Modal Transport Systems
Applied Sciences,
Год журнала:
2025,
Номер
15(10), С. 5709 - 5709
Опубликована: Май 20, 2025
In
recent
years,
several
emerging
transport
modes
have
appeared
in
cities
all
over
the
world
and
been
widely
adopted
by
commuters
travelers.
This
leads
to
strong
growth
popularity
of
multi-modal
Mobility
as
a
Service
(MaaS)
cities.
These
not
only
received
much
attention
from
service
providers
practitioners
but
also
attracted
researchers
related
communities.
are
reflected
growing
number
published
papers
research
issues
mobility
The
factors
that
driving
deficiencies
effective
solution
methods
accommodate
needs
users
with
modes.
Although
existing
literature
is
still
deficient
offering
seamless
end-to-end
services,
it
provides
valuable
sources
clues
for
finding
potential
future
subjects/issues/directions.
this
study,
we
attempt
identify
directions
based
on
review
transport.
By
searching
WOS
database,
analyze
profile
trends
mobility.
results
study
pave
way
assessment
subjects/issues/directions
under
umbrella
term
paper
significantly
reduces
time
required
readers
prospective
subjects,
issues,
or
without
delving
into
literature.
Язык: Английский
A Survey of Artificial Intelligence-Related Cybersecurity Risks and Countermeasures in Mobility-as-a-Service
IEEE Intelligent Transportation Systems Magazine,
Год журнала:
2024,
Номер
16(6), С. 37 - 55
Опубликована: Авг. 5, 2024
Mobility-as-a-Service
(MaaS)
integrates
different
transport
modalities
and
can
support
more
personalisation
of
travellers'
journey
planning
based
on
their
individual
preferences,
behaviours
wishes.
To
fully
achieve
the
potential
MaaS,
a
range
AI
(including
machine
learning
data
mining)
algorithms
are
needed
to
learn
personal
requirements
needs,
optimise
each
traveller
all
travellers
as
whole,
help
service
operators
relevant
governmental
bodies
operate
plan
services,
detect
prevent
cyber
attacks
from
various
threat
actors
including
dishonest
malicious
operators.
The
increasing
use
processing
in
both
centralised
distributed
settings
opens
MaaS
ecosystem
up
diverse
privacy
at
algorithm
level
connectivity
surfaces.
In
this
paper,
we
present
first
comprehensive
review
coupling
between
AI-driven
design
security
challenges
related
countermeasures.
particular,
focus
how
current
emerging
AI-facilitated
risks
(profiling,
inference,
third-party
threats)
adversarial
(evasion,
extraction,
gamification)
may
impact
ecosystem.
These
often
combine
novel
(e.g.,
inverse
learning)
with
traditional
attack
vectors
man-in-the-middle
attacks),
exacerbating
for
wider
participation
emergence
new
business
models.
Язык: Английский