Buildings,
Год журнала:
2024,
Номер
14(9), С. 2978 - 2978
Опубликована: Сен. 20, 2024
Although
it
is
well
established
that
thermal
environments
significantly
influence
travel
behavior,
the
synergistic
effects
of
points
interest
(POI)
and
on
behavior
remain
unclear.
This
study
developed
a
vision-based
outdoor
evaluation
model
aimed
at
uncovering
driving
factors
behind
human
in
spaces.
First,
Yolo
v5
questionnaires
were
employed
to
obtain
crowd
activity
intensity
preference
levels.
Subsequently,
target
detection
clustering
algorithms
used
derive
variables
such
as
POI
attractiveness
distance,
while
validated
environmental
simulator
was
utilized
simulate
comfort
distributions
across
different
times.
Finally,
multiple
classification
models
compared
establish
mapping
relationships
between
POI,
environment
variables,
preferences,
with
SHAP
analysis
examine
contribution
each
variable.
The
results
indicate
XGBoost
achieved
best
predictive
performance
(accuracy
=
0.95),
shadow
proportion
(|SHAP|
0.24)
distance
0.12)
identified
most
significant
influencing
preferences.
By
extrapolation,
this
can
provide
valuable
insights
for
optimizing
community
enhancing
vitality
areas
similar
climatic
cultural
contexts.
Buildings,
Год журнала:
2024,
Номер
14(9), С. 2978 - 2978
Опубликована: Сен. 20, 2024
Although
it
is
well
established
that
thermal
environments
significantly
influence
travel
behavior,
the
synergistic
effects
of
points
interest
(POI)
and
on
behavior
remain
unclear.
This
study
developed
a
vision-based
outdoor
evaluation
model
aimed
at
uncovering
driving
factors
behind
human
in
spaces.
First,
Yolo
v5
questionnaires
were
employed
to
obtain
crowd
activity
intensity
preference
levels.
Subsequently,
target
detection
clustering
algorithms
used
derive
variables
such
as
POI
attractiveness
distance,
while
validated
environmental
simulator
was
utilized
simulate
comfort
distributions
across
different
times.
Finally,
multiple
classification
models
compared
establish
mapping
relationships
between
POI,
environment
variables,
preferences,
with
SHAP
analysis
examine
contribution
each
variable.
The
results
indicate
XGBoost
achieved
best
predictive
performance
(accuracy
=
0.95),
shadow
proportion
(|SHAP|
0.24)
distance
0.12)
identified
most
significant
influencing
preferences.
By
extrapolation,
this
can
provide
valuable
insights
for
optimizing
community
enhancing
vitality
areas
similar
climatic
cultural
contexts.
