Fueling Change for Sustainability? On the Role of Society and Public Administrations to Promote Zero-Emission Delivery Initiatives
Опубликована: Янв. 1, 2025
Язык: Английский
Decoding cargo bikes’ potential to be a sustainable last-mile delivery mode: an operations management perspective
Transportation Planning and Technology,
Год журнала:
2024,
Номер
unknown, С. 1 - 23
Опубликована: Июль 10, 2024
Cargo
bikes
are
considered
as
a
low-cost
and
flexible
last-mile
solution
for
the
transport
of
goods.
However,
there
few
studies
that
identify
contextualise
factors
underpinning
their
sustainable
operations
potential
to
effectively
work
last
leg
green,
efficient,
societally
beneficial
supply
chain.
The
authors
addressed
this
gap
by
systematically
collecting
thematically
analysing
49
articles
published
between
2017
2023.
findings
demonstrate
cargo
can
utilise
delivery
mode
if:
(a)
optimised
(from
parking
routing
from
traffic
management
load
capacity
planning);
(b)
social
sustainability
performance
is
enhanced
(e.g.
safety,
security,
fatigue
workforce);
(c)
cities
hosting
them
invest
in
bike-friendly
infrastructure,
regulatory
frameworks,
land
use
approaches
mobility
hubs.
This
paper
offers
bike
insights
assist
relevant
stakeholders
enhance
efficiency
overall
adoption.
Язык: Английский
Optimization of Charging Infrastructure for Electric Micromobility Vehicles in Touristic Areas
Опубликована: Июнь 25, 2024
Язык: Английский
Predicting Cyclist Speed in Urban Contexts: A Neural Network Approach
Modelling—International Open Access Journal of Modelling in Engineering Science,
Год журнала:
2024,
Номер
5(4), С. 1601 - 1617
Опубликована: Ноя. 5, 2024
Bicycle
use
has
become
more
important
today,
but
information
and
planning
models
are
needed
to
implement
bike
lanes
that
encourage
cycling.
This
study
aimed
develop
a
methodology
predict
the
speed
cyclist
can
reach
in
an
urban
environment
provide
for
cycling
infrastructure.
The
consisted
of
obtaining
GPS
data
on
longitude,
latitude,
elevation,
time
from
smartphone
two
groups
cyclists
calculate
speeds
slopes
through
model
based
recurrent
short-term
memory
(LSTM)
type
neural
network.
was
trained
70%
dataset,
with
remaining
30%
used
validation
varying
training
epochs
(100,
200,
300,
600).
effectiveness
networks
predicting
is
shown
determination
coefficients
0.77
0.96.
Average
ranged
6.1
20.62
km/h.
provides
new
offers
valuable
various
applications
transportation
bicycle
line
planning.
A
limitation
be
variability
device
accuracy,
which
could
affect
measurements
generalizability
findings.
Язык: Английский