A review of multilayer networks-based interregional transportation networks analysis
Chaos Solitons & Fractals,
Год журнала:
2025,
Номер
192, С. 115993 - 115993
Опубликована: Янв. 16, 2025
Язык: Английский
Motion-Inspired Spatial–Temporal Transformer for accurate vessel trajectory prediction
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
148, С. 110391 - 110391
Опубликована: Март 6, 2025
Язык: Английский
Skip or not: Hybrid machine learning for decision support in strategic port-skipping behavior to enhance liner shipping reliability
Ocean Engineering,
Год журнала:
2025,
Номер
324, С. 120730 - 120730
Опубликована: Фев. 22, 2025
Язык: Английский
Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 13, 2025
Effective
fisheries
management
is
the
key
to
achieve
sustainable
globally,
while
accurate
monitoring
of
fishing
vessels
essential
improve
effectiveness
measures.
Self-reported
information
on
vessel
types
often
limited
and
may
not
cover
all
operating
vessels,
causing
incomplete
in
management.
Therefore,
a
novel
way
objectively
identify
large
quantity
needed.
In
this
study,
we
presented
an
innovative
integrated
deep
learning
model
by
using
automatic
identification
system
(AIS)
data
classify
five
including
gillnetter,
hook
liner,
trawler,
fish
carrier,
stow
net
vessel,
further
improving
performance
classification.
First,
preprocessed
removing
erroneous
information,
dividing
trajectories
day
obtain
complete
reliable
dataset.
Then,
multidimensional
feature
vector
was
constructed
combining
geometric,
static
dynamic
characteristics
explain
behavioral
differences
various
more
effectively.
Finally,
fed
into
ensemble
two-dimensional
bidirectional
long
short-term
memory
network
convolutional
neural
with
attention
mechanism
for
training,
prediction
results
were
obtained
through
fully
connected
layer.
The
accuracy
91.90%,
which
higher
than
other
single
classifiers.
experimental
demonstrated
that
method
remarkable
could
be
adopted
precision
classification
based
AIS
data.
Язык: Английский
Research on ship dynamic feature extraction and prediction method based on visual data
Ocean Engineering,
Год журнала:
2025,
Номер
327, С. 120938 - 120938
Опубликована: Март 17, 2025
Язык: Английский
Deep multi-view information-powered vessel traffic flow prediction for intelligent transportation management
Transportation Research Part E Logistics and Transportation Review,
Год журнала:
2025,
Номер
197, С. 104072 - 104072
Опубликована: Март 21, 2025
Язык: Английский
A framework for ship semantic behavior representation and indexing
Ocean Engineering,
Год журнала:
2025,
Номер
329, С. 121023 - 121023
Опубликована: Апрель 7, 2025
Язык: Английский
BESO-PPF: A PPF-optimized ship heading controller based on backstepping control and the ESO
Ocean Engineering,
Год журнала:
2024,
Номер
316, С. 119925 - 119925
Опубликована: Дек. 3, 2024
Язык: Английский
Parameter-Efficient Vehicle Trajectory Prediction Based on Attention-Enhanced Liquid Structural Neural Model
World Electric Vehicle Journal,
Год журнала:
2024,
Номер
16(1), С. 19 - 19
Опубликована: Дек. 31, 2024
Due
to
advances
in
sensor
techniques
and
deep
learning,
autonomous
vehicular
technologies
have
become
more
reliable
practical.
Trajectory
prediction
is
a
critical
task
anticipate
the
future
positions
of
surrounding
vehicles.
However,
existing
algorithms,
such
as
LSTM-based
attention-based
models,
face
challenges
high
computational
complexity,
large
parameter
sizes,
limited
ability
efficiently
capture
both
temporal
dependencies
spatial
interactions
dynamic
traffic
scenarios.
In
this
paper,
we
propose
parameter-efficient
trajectory
model
that
integrates
Liquid
Time-Constant
(LTC)
networks
with
attention
mechanisms,
termed
Attn-LTC
model.
The
key
contributions
our
work
are
threefold.
First,
introduce
attention-enhanced
LTC
encoder
effectively
captures
long-term
behaviors
from
historical
data.
Second,
incorporate
decoder,
which
emphasizes
influence
neighboring
vehicles
interactions,
thereby
improving
accuracy.
Third,
demonstrate
efficiency
model,
achieves
predictive
accuracy
significantly
fewer
parameters
compared
Transformer-based
counterparts.
Extensive
experiments
conducted
on
NGSIM
dataset
advantages
proposed
Notably,
it
reduces
complexity
size
while
maintaining
superior
accuracy,
making
well
suited
for
deployment
resource-constrained
systems.
results
highlight
effectiveness
balancing
precision
efficiency,
paving
way
its
application
real-time
driving
Язык: Английский