Advances in logistics, operations, and management science book series,
Год журнала:
2024,
Номер
unknown, С. 191 - 244
Опубликована: Дек. 20, 2024
Artificial
intelligence
has
made
great
strides
in
various
fields,
especially
improving
logistics
operations
and
freight
transportation.
This
chapter
aims
to
highlight
the
importance
of
applying
AI
manage
sprawl
phenomenon.
The
research
focused
on
analyzing
impact
use
performance
urban
transport
under
sprawling
conditions.
To
achieve
this,
authors
carried
out
a
literature
review
explore
different
categories
including
machine
learning,
deep
natural
language
processing,
visual
data
reinforcement
learning
(RL),
specialized
algorithms,
optimise
activities
within
context.
Irish Interdisciplinary Journal of Science & Research,
Год журнала:
2024,
Номер
08(02), С. 123 - 131
Опубликована: Янв. 1, 2024
This
proposed
work
presents
the
WiFi
technology-based
approach
for
dual
module-based
vehicle
speed
governance
in
fledgling
zones.
The
second
module
should
be
inside
car
and
centralized
transmitter
middle
of
limited
area.
device
sends
limits
other
information
to
receiver
unit,
which
dynamically
controls
within
zone.
acts
as
a
command
control
station
continuously
reports
zone-based
enforcement
maximum
value
criteria.
It
meets
same
standards
with
innovative
algorithms
that
adapt
road
conditions.
immobilizes
vehicle,
so
take
action
spend
maintain
signaled
speed.
scheme
includes
real-time
data
transmission
condition
adaptation.
WiFi-based
system
allows
scalable
low-cost
restrictions
Simulations
field
tests
show
can
reduce
traffic,
improve
safety,
boost
transportation
efficiency.
Our
map
may
include
adding
sensors
communication
protocols
position
our
any
use
case
increase
its
capability.
Finally,
using
modules
systems
restricted
areas
could
solve
traffic
issues
defragment
network.
paper
is
positive
step
toward
technology
safety
urban
mobility.
International Journal of Advanced Computer Science and Applications,
Год журнала:
2024,
Номер
15(7)
Опубликована: Янв. 1, 2024
The
steady
progression
of
information
technology
today
is
opening
up
opportunities
for
extensive
automation
across
various
sectors,
including
the
automotive
industry.
active
development
IT
systems
has
paved
way
V2X
(Vehicle-to-Everything)
technology,
which
enables
communication
such
as
"vehicle-to-vehicle"
and
"vehicle-to-road
infrastructure".
This
article
focuses
on
exploring
use
to
create
"intelligent
transportation".
Currently,
technologies
are
not
widely
adopted
due
limited
coverage
5G
networks.
Although
existing
4G
network
adequate
streaming
HD
content
playing
online
games,
it
cannot
support
safer
smarter
operation
required
autonomous
cars.
Nevertheless,
within
framework,
possible
develop
a
comprehensive
solution
automating
car
traffic.
would
significantly
reduce
number
road
accidents
optimize
traffic
flow.
explores
implementation
in
achieve
these
goals.
Scientific Journal of Astana IT University,
Год журнала:
2024,
Номер
unknown, С. 31 - 47
Опубликована: Окт. 30, 2024
This
research
applies
deep
neural
networks
(DNN)
and
convolutional
(CNN)
to
the
modeling
prediction
of
driving
behavior
in
autonomous
vehicles
within
Smart
City
context.
Developed,
trained,
validated,
tested
Keras
framework,
model
is
optimized
predict
steering
angle
for
self-driving
a
controlled
simulated
environment.
Utilizing
training
dataset
comprised
image
data
paired
with
angles,
achieves
navigation
along
designated
track.
Key
innovations
model’s
architecture,
including
parameter
fine-tuning
structural
optimization,
contribute
its
computational
efficiency
high
responsiveness.
The
integration
layers
facilitates
advanced
spatial
feature
extraction,
while
inclusion
repeated
mitigates
information
loss,
implications
potential
future
enhancements.
Clustering
algorithms,
K-Means,
DBSCAN,
Gaussian
Mixture
Model,
Mean-Shift,
Hierarchical
Clustering,
further
augment
by
providing
insights
into
environment
segmentation,
obstacle
detection,
pattern
analysis,
thereby
enhancing
complex
decision-making
capabilities
amid
real-
world
noise
uncertainty.
Empirical
results
demonstrate
efficacy
DBSCAN
algorithms
addressing
environmental
uncertainties,
displaying
robust
tolerance
anomaly
detection
capabilities.
Additionally,
CNN
exhibits
superior
performance,
lower
loss
values
on
both
validation
datasets
compared
an
RNN
model,
underscoring
CNN’s
suitability
visually
driven
tasks
systems.
study
advances
field
vehicle
through
novel
clustering
support
sophisticated
driving.
findings
development
intelligent
systems
emphasizing
precision
efficiency.
Advances in logistics, operations, and management science book series,
Год журнала:
2024,
Номер
unknown, С. 191 - 244
Опубликована: Дек. 20, 2024
Artificial
intelligence
has
made
great
strides
in
various
fields,
especially
improving
logistics
operations
and
freight
transportation.
This
chapter
aims
to
highlight
the
importance
of
applying
AI
manage
sprawl
phenomenon.
The
research
focused
on
analyzing
impact
use
performance
urban
transport
under
sprawling
conditions.
To
achieve
this,
authors
carried
out
a
literature
review
explore
different
categories
including
machine
learning,
deep
natural
language
processing,
visual
data
reinforcement
learning
(RL),
specialized
algorithms,
optimise
activities
within
context.
