IET Intelligent Transport Systems,
Год журнала:
2025,
Номер
19(1)
Опубликована: Янв. 1, 2025
ABSTRACT
A
novel
scheme
is
proposed
for
the
distributed
multi‐ship
collision
avoidance
(CA)
problem
with
consideration
of
autonomous,
dynamic
nature
real
circumstance.
All
ships
in
envisioned
scenarios
can
share
their
decisions
or
intentions
through
route
exchange,
allowing
them
to
make
subsequent
based
on
planning
each
iteration.
By
leveraging
CA
involves
iterations
negotiation,
and
regarded
as
a
staged
cooperative
game
under
conditions
complete
information.
The
concept
closest
spatio‐temporal
distance
(CSTD)
introduced
more
accurately
assess
risk
between
ships.
coordinated
mechanism
established
when
identified,
which
further
incorporates
considerations
including
stand‐on/give‐way
relationships,
negotiation
rounds,
re‐planning
calculation,
well
cost
factor
evaluation.
Nash
bargaining
solution
(NBS)
elaborated
achieve
Pareto‐optimal
routes
scenarios.
In
model,
while
individual
interest
ship
are
maximized,
economic
fairness
global
optimization
overall
system
also
maintained.
Simulation
results
indicate
that
NBS
shows
good
flexibility
adaptability,
all
comply
solution,
bring
out
normal
solutions
within
limited
number
iterations.
Journal of Marine Science and Engineering,
Год журнала:
2024,
Номер
12(10), С. 1728 - 1728
Опубликована: Окт. 1, 2024
With
the
global
economy’s
relentless
growth
and
heightened
environmental
consciousness,
sustainable
maritime
transport
emerges
as
a
pivotal
development
trajectory
for
shipping
sector.
This
study
systematically
analyzes
478
publications
searched
in
Web
of
Science
Core
Collection,
from
2000
to
2023,
utilizing
bibliometric
methods
investigate
application
areas
within
industry.
begins
with
an
analysis
annual
publication
trends,
which
reveals
substantial
expansion
research
endeavors
this
discipline
over
recent
years.
Subsequently,
comprehensive
statistical
evaluation
scholarly
journals
collaborative
network
assessment
are
conducted
pinpoint
foremost
productive
journals,
nations,
organizations,
individual
researchers.
Furthermore,
keyword
co-occurrence
methodology
is
applied
delineate
core
themes
emerging
focal
points
domain,
thereby
outlining
potential
directions
future
research.
In
addition,
drawing
on
analysis,
advancements
intelligent
technologies
green
port
construction
applications
discussed.
Finally,
review
discusses
existing
challenges
opportunities
theoretical
practical
perspective.
The
shows
that,
terms
technology,
data
security
multi-source
focus
that
people
need
pay
attention
future;
prediction
different
climates
ship
types
also
area
ports,
Cold
Ironing
(CI)
one
key
strategy,
how
drive
stakeholders
build
ports
efficiently
economically
developmental
direction.
serves
enhance
researchers’
comprehension
current
landscape
progression
technologies,
fostering
continued
advancement
exploration
vital
domain.
Journal of Marine Science and Engineering,
Год журнала:
2024,
Номер
12(4), С. 624 - 624
Опубликована: Апрель 7, 2024
Artificial
intelligence
(AI)
technologies
are
increasingly
being
applied
to
the
shipping
industry
advance
its
development.
In
this
study,
476
articles
published
in
Science
Citation
Index
Expanded
(SCI-EXPANDED)
and
Social
Sciences
(SSCI)
of
Web
Core
Collection
from
2001
2022
were
collected,
bibliometric
methods
conduct
a
systematic
literature
field
AI
technology
applications
industry.
The
review
commences
with
an
annual
publication
trend
analysis,
which
shows
that
research
has
been
growing
rapidly
recent
years.
This
is
followed
by
statistical
analysis
journals
collaborative
network
identify
most
productive
journals,
countries,
institutions,
authors.
keyword
“co-occurrence
analysis”
then
utilized
major
clusters,
as
well
hot
directions
field,
providing
for
future
field.
Finally,
based
on
results
co-occurrence
content
papers
years,
gaps
AIS
data
applications,
ship
trajectory,
anomaly
detection,
possible
directions,
discussed.
findings
indicate
direction
mainly
reflected
behavior
repair.
Ship
trajectory
deep
learning-based
method
discussion
classification.
Anomaly
detection
application
learning
improving
efficiency
detection.
These
insights
offer
guidance
researchers’
investigations
area.
addition,
we
discuss
implications
both
theoretical
practical
perspectives.
Overall,
can
help
researchers
understand
status
development
shipping,
correctly
grasp
methodology,
promote
further
Transportation Research Part E Logistics and Transportation Review,
Год журнала:
2023,
Номер
181, С. 103367 - 103367
Опубликована: Дек. 6, 2023
It
is
critical
to
have
accurate
ship
trajectory
prediction
for
collision
avoidance
and
intelligent
traffic
management
of
manned
ships
emerging
Maritime
Autonomous
Surface
Ships
(MASS).
Deep
learning
methods
based
on
AIS
data
emerged
as
a
contemporary
maritime
transportation
research
focus.
However,
concerns
about
its
accuracy
computational
efficiency
widely
exist
across
both
academic
industrial
sectors,
necessitating
the
discovery
new
solutions.
This
paper
aims
develop
approach
called
Bi-Directional
Information-Empowered
(DBDIE)
by
utilising
integrated
multiple
networks
an
attention
mechanism
address
above
issues.
The
DBDIE
model
extracts
valuable
features
fusing
Bi-directional
Long
Short-Term
Memory
(Bi-LSTM)
Gated
Recurrent
Unit
(Bi-GRU)
neural
networks.
Additionally,
weights
two
bi-directional
units
are
optimised
using
mechanism,
final
results
obtained
through
weight
self-adjustment
mechanism.
effectiveness
proposed
verified
comprehensive
comparisons
with
state-of-the-art
deep
methods,
including
Neural
Network
(RNN),
(LSTM),
(GRU),
Bi-LSTM,
Bi-GRU,
Sequence
(Seq2Seq),
Transformer
experimental
demonstrate
that
achieves
most
satisfactory
outcomes
than
all
other
classical
providing
solution
improving
predicting
trajectories,
which
becomes
increasingly
important
in
era
safe
navigation
mixed
MASS.
As
result,
findings
can
aid
development
implementation
proactive
preventive
measures
avoid
collisions,
enhance
efficiency,
ensure
safety.
Transportation Research Part C Emerging Technologies,
Год журнала:
2024,
Номер
163, С. 104648 - 104648
Опубликована: Май 9, 2024
Automatic
Identification
System
(AIS)
offers
a
wealth
of
vessel
navigation
data,
which
underpins
research
in
maritime
data
mining,
situational
awareness,
and
knowledge
discovery
within
the
realm
intelligent
transportation
systems.
The
flourishing
marine
industry
has
prompted
AIS
satellites
base
stations
to
generate
massive
amounts
trajectory
escalating
both
storage
calculation
costs.
conventional
Douglas-Peucker
(DP)
algorithm
used
for
compression
sets
uniform
threshold,
hampers
effective
compression.
Additionally,
compressing
accelerating
computation
large
datasets
poses
significant
challenge
real-world
applications.
To
address
these
limitations,
this
paper
aims
develop
new
Graphics
Processing
Unit
(GPU)
parallel
computing
framework
that
enables
acceleration
optimal
threshold
each
automatically
big
mining.
It
achieves
by
incorporating
Adaptive
DP
with
Speed
Course
(ADPSC)
algorithm,
utilizes
dynamic
characteristics
different
vessels.
can
effectively
solve
associated
computational
time
cost
concern
when
using
ADPSC
compress
vast
real
world.
proposes
novel
evaluation
metric
assessing
efficacy
based
on
Dynamic
Time
Warping
(DTW)
method.
Comprehensive
experiments
encompass
from
three
representative
areas:
Tianjin
Port,
Chengshan
Jiao
Promontory,
Caofeidian
Port.
experimental
results
demonstrate
1)
newly
developed
method
outperforms
terms
compression,
2)
designed
GPU
significantly
shorten
extensive
datasets.
GPU-accelerated
methodology
not
only
minimizes
transmission
costs
manned
unmanned
vessels
but
also
enhances
processing
speed,
supporting
real-time
decision-making.
From
theoretical
perspective,
it
provides
key
puzzle
realizing
anti-collision
ships,
particularly
complex
waters.
hence
makes
contributions
safety
autonomous
shipping
era.
Engineering Applications of Artificial Intelligence,
Год журнала:
2024,
Номер
133, С. 108172 - 108172
Опубликована: Март 8, 2024
Ship
Time
Headway
(STH)
is
used
in
maritime
navigation
to
describe
the
time
interval
between
arrivals
of
two
consecutive
ships
same
water
area.
This
measurement
may
offer
a
straightforward
way
gauge
frequency
ship
traffic
and
likelihood
congestion
particular
STH
an
important
factor
understanding
managing
dynamics
movements
busy
waterways.
paper
introduces
hybrid
deep
learning
method
for
predicting
domain.
The
integrates
Seasonal-Trend
Decomposition
using
Loess
(STL),
Multi-head
Self-Attention
(MSA)
mechanism
into
Long
Short-Term
Memory
(LSTM)
neural
network.
dataset
was
extracted
from
Automatic
Identification
System
(AIS)
through
trajectory
spatial
motion,
seasonal,
trend
residual
components
decomposition
were
then
determined
STL
algorithms.
MSA-LSTM
adopted
comprehensively
capture
evolving
patterns
sequence.
Comparison
studies
with
existing
methods
demonstrate
accuracy
robustness
predictions
provided
by
this
method,
indicating
that
proposed
outperforms
other
models
terms
prediction
performance
capabilities.
By
STH,
offers
potential
assist
managers
navigators
assessing
flow,
thereby
enabling
them
make
informed
decisions
on
safety
efficiency.