Using Big Data for Predictive Maintenance in Transportation Systems
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 297 - 314
Опубликована: Апрель 4, 2025
The
economic
and
social
health
of
contemporary
urban
centers
is
greatly
dependent
on
the
transportation
industry.
Transportation
infrastructure
must
be
dependable
efficient
because
any
disruptions
can
have
a
domino
effect
mobility
as
whole.
Predictive
maintenance,
facilitated
by
analysis
big
data,
gives
chance
to
proactively
address
maintenance
needs
minimize
service
interruptions.
use
data
analytics
for
predictive
in
systems
examined
this
chapter.
It
starts
going
over
special
sources
that
are
available
industry,
such
sensor
from
infrastructure,
cars,
traffic
control
systems.
explores
essential
phases
procedure,
encompassing
gathering,
analysis,
modeling,
production
practical
insights.
application
data-driven
various
contexts—such
public
fleets,
road
rail
networks—is
demonstrated
through
several
case
studies.
Язык: Английский
Exploring digital twins for transport planning: a review
European Transport Research Review,
Год журнала:
2025,
Номер
17(1)
Опубликована: Март 13, 2025
Язык: Английский
Digital twin-driven management strategies for logistics transportation systems
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 9, 2025
With
the
development
of
Industry
5.0,
logistics
industry,
serving
as
a
bridge
between
production
and
consumption,
is
undergoing
profound
changes.
However,
this
transformation
faces
challenges
such
data
fragmentation,
difficult
system
integration,
insufficient
real-time
monitoring
capabilities.
Consequently,
modern
demands
higher
standards
for
prediction
management
transportation
behavior.
To
address
these
challenges,
paper
introduces
Digital
Twin
(DT)
technology
proposes
research
methodology
DT-driven
strategies.
DT
constructs
virtual
models
physical
objects
to
enable
analysis
unmanned
vehicle
states,
effectively
resolving
identified
issues.
Specifically,
proposed
method
leverages
integrate
multi-source
heterogeneous
establishes
digital
model
vehicles.
Furthermore,
it
combines
LSTM
neural
network
algorithm
design
predictive
time-series
forecasting
behaviors.
The
dynamically
adjusted
based
on
results,
further
optimizing
strategy.
Finally,
effectiveness
validated
through
case
study
Experimental
results
demonstrate
that
DT-based
strategy
significantly
improves
accuracy
predicting
behaviors
exhibits
superior
performance
in
decision
aid
fault
tolerance.
Additionally,
simulation
tests
confirm
reliability
efficiency
improved
practical
applications,
providing
an
important
reference
intelligent
systems.
Язык: Английский
Integration of Pavement Finite Element simulation with Digital Twin: Current Practices, Emerging Trends, and Future Enablers
Journal of Information Technology in Construction,
Год журнала:
2025,
Номер
30, С. 544 - 569
Опубликована: Апрель 19, 2025
In
the
transition
towards
Construction
5.0,
intelligent
systems,
such
as
predictive
Digital
Twins
(DTs),
have
emerged
a
critical
solution
in
infrastructure
assets
management.
This
is
by
leveraging
advanced
simulations
and
analytical
methods
for
accurate
asset
condition
prediction.
However,
while
are
essential
enabling
DTs,
existing
literature
often
overlooks
role
of
pavement
simulation
within
developed
DTs.
paper
systematically
leverages
on
Finite
Element
(FE)
modelling
performance
prediction
to
assess
current
state
practice
simulations,
identifies
trends
integration,
proposes
advancements
enhance
incorporation
FE
models
an
architecture
integration.
Finally,
study
concludes
with
call
future
research
directions
address
gaps,
aiming
advance
DTs
sustainable
Язык: Английский