Noise abatement and traffic safety: The trade-off of quieter engines and pavements on vehicle detection

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Please
cite
this
article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
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Model
AAP
2932
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Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx
Contents
lists
available
at
SciVerse
ScienceDirect
Accident
Analysis
and
Prevention
j
ourna
l
h
o
mepage:
www.elsevier.com/locate/aap
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection
1
2
C.
Mendonc¸
aa,d,∗,
E.
Freitasb,
J.P.
Ferreirab,
I.D.
Raimundob,
J.A.
Santosa,c,d
Q1
3
aSchool
of
Psychology,
Department
of
Basic
Psychology,
University
of
Minho,
Portugal4
bSchool
of
Engineering,
Department
of
Civil
Engineering,
University
of
Minho,
Portugal5
cCentro
Algoritmi,
University
of
Minho,
Portugal6
dCentro
de
Computac¸
ão
Gráfica,
Portugal7
8
a
r
t
i
c
l
e
i
n
f
o9
10
Article
history:11
Received
19
June
201212
Received
in
revised
form
8
September
201213
Accepted
26
October
2012
14
15
Keywords:16
Signal
detection
17
Road
traffic
safety18
Noise19
Vehicles20
Dense
asphalt21
Open asphalt rubber
22
Cobble
stones23
Psychoacoustic
analysis24
a
b
s
t
r
a
c
t
Road
traffic
sounds
are
a
major
source
of
noise
pollution
in
urban
areas.
But
recent
developments
such
as
low
noise
pavements
and
hybrid/electric
engine
vehicles
cast
an
optimistic
outlook
over
such
an
environ-
mental
problem.
However,
it
can
be
argued
that
engine,
tire,
and
road
noise
could
be
relevant
sources
of
information
to
avoid
road
traffic
conflicts
and
accidents.
In
this
paper,
we
analyze
the
potential
trade-offs
of
traffic-noise
abatement
approaches
in
an
experimental
study,
focusing
for
the
first
time
on
the
impact
and
interaction
of
relevant
factors
such
as
pavement
type,
vehicle
type,
listener’s
age,
and
background
noise,
on
vehicle
detection
levels.
Results
reveal
that
vehicle
and
pavement
type
significantly
affect
vehi-
cle
detection.
Age
is
a
significant
factor,
as
both
younger
and
older
people
exhibit
lower
detection
levels
of
incoming
vehicles.
Low
noise
pavements
combined
with
all-electric
and
hybrid
vehicles
might
pose
a
severe
threat
to
the
safety
of
vulnerable
road
users.
All
factors
interact
simultaneously,
and
vehicle
detection
is
best
predicted
by
the
loudness
signal-to-noise
ratio.
© 2012 Published by Elsevier Ltd.
1.
Introduction25
Traffic
related
noise
is
nowadays
the
major
source
of
environ-26
mental
noise
in
most
industrialized
nations
and
developing
regions.
27
Its
negative
impact
has
been
demonstrated
at
several
instances,
28
from
health
to
school
efficiency
and
overall
emotional
annoyance29
(e.g.,
Gorai
and
Pal,
2006;
Passchier-Vermeer
and
Passchier,
2000;30
Sanz
et
al.,
1993;
Freitas
et
al.,
2012).
It
is
therefore
a
matter
of
active31
concern
for
traffic-related
researchers,
public
authorities
in
health32
and
traffic,
as
well
as
transportation
and
road
industries,
to
find33
quieter
alternatives
to
the
major
sources
of
transportation
noise.
34
In
a
near
future,
we
might
expect
a
reduction
of
road
traffic
noise
35
both
by
pavements
that
are
more
efficient
and
because
of
the
grow-36
ing
popularity
of
hybrid
and
all-electric
vehicles.
Therefore,
there37
is
an
optimistic
outlook
on
health
improvement
and
annoyance38
reduction
due
to
a
quieter
road
traffic
environment,
specifically
for39
populations
living
in
urban
areas.40
However,
in
urban
areas
traffic
noise
could
also
be
a
key
fac-
41
tor
for
the
awareness
of
imminent
conflicts
by
vulnerable
road
42
users.
In
other
words,
road,
tire
and
engine
noises
might
be
used43
∗Corresponding
author
at:
Centro
de
Computac¸
ão
Gráfica,
Campus
de
Azurém,
4800-058
Guimarães,
Portugal.
Tel.:
+351
253
510
580;
fax:
+351
253
510
581.
E-mail
address:
catarina.mendonca@ccg.pt (C.
Mendonc¸
a).
as
meaningful
signals
by
pedestrians
and
bicyclists:
they
can
act
as
44
attentional
triggers,
allowing
for
a
better
perception
of
speed
and
45
proximity
of
incoming
traffic
and
for
timely
reactions
to
avoid
con-
46
flicts.
Therefore,
due
to
traffic
noise
abatement,
we
might
face
in
47
the
near
future
an
increasing
trade-off
between
the
improvement
48
of
population’s
health
and
the
rise
of
accidents
involving
vulnera-
49
ble
road
users.
Such
trade-off
analysis
has
never
been
approached
50
from
an
experimental
perspective.
51
When
compared
to
internal
combustion
vehicles,
elec-
52
tric/hybrid
engine
vehicles
have
higher
incidence
of
crashes
53
involving
pedestrians
and
bicyclists
(Garay-Vega
et
al.,
2010;
54
Hanna,
2009).
On
the
one
hand
road
users
show
substantial
interest
55
in
driving
quiet
hybrid
or
all-electric
cars;
but
on
the
other
hand
56
they
are
concerned
with
the
reduced
conspicuity
of
such
vehicles
57
(Wolgater
et
al.,
2001).
Some
experimental
studies
have
addressed
58
this
issue.
Ashmead
et
al.
(2012)
analyzed
the
path
identification
59
of
electric
engine
and
internal
combustion
engine
vehicles
in
quiet
60
and
noisy
environments.
They
found
that
in
quiet
environments
61
there
were
timely
path
identifications
of
the
electrical
vehicles,
62
but
not
in
noisy
ones.
They
also
found
that
these
judgments
were
63
based
on
sound
level,
the
main
characteristic
that
is
altered
in
elec-
64
tric/hybrid
cars.
Studies
with
visually
impaired
populations
have
65
also
revealed
lower
vehicle
detectability
of
hybrid
and
all-electric 66
vehicles
(Emerson
et
al.,
2010).
All
these
data
have
contributed
to
67
the
official
recognition
by
the
U.S.
National
Highway
Traffic
Safety
68
0001-4575/$
–
see
front
matter ©
2012 Published by Elsevier Ltd.
http://dx.doi.org/10.1016/j.aap.2012.10.018

Please
cite
this
article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
G
Model
AAP
2932
1–7
2C.
Mendonc¸
a
et
al.
/
Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx
Administration
that
electric
vehicles
in
low-speed
operation
may69
induce
a
safety
issue
for
blind
pedestrians
(Garay-Vega
et
al.,
2010).70
Other
factors
might
affect
traffic-related
noise
and
hence
vehi-71
cle
conspicuity.
In
a
previous
study
(Freitas
et
al.,
2012)
we
have72
demonstrated
that
pavement
type
largely
affects
the
levels
of
envi-73
ronmental
noise
and
related
subjective
annoyance.
However,
the74
way
pavement
type
affects
vehicle
detection
is
still
not
clear.
In75
addition,
age
might
be
regarded
as
a
relevant
variable.
Young
pede-76
strians
are
more
often
involved
in
accidents
than
older
people
are,77
but
while
being
rare,
accidents
with
older
people
are
the
most78
severe
(Martin,
2006).
In
experimental
studies
with
children,
the79
number
of
correctly
identified
vehicle
sounds
was
significantly80
improved
with
age
(Pfeffer
and
Barnecutt,
1996).
Despite
the
strong81
evidence
of
the
role
of
several
traffic
noise
factors
on
vehicle
con-82
spicuity,
there
has
never
been
a
comprehensive
study
analyzing
the83
main
relevant
variables
(Barton
et
al.,
2012).84
In
this
paper,
we
present
for
the
first
time
such
an
integrated85
approach
to
traffic
noise
variables
and
related
vehicle
detection
lev-86
els.
We
address
the
detection
of
approaching
vehicles
as
a
function87
of
pavement,
vehicle
type,
background
noise
and
the
age
of
the
lis-88
tener.
Binaural
pass-by
noise
samples
were
recorded
using
several
89
combinations
of
pavement,
vehicle
and
speed.
These
samples
were90
then
edited
to
create
scenarios
of
approaching
vehicles
in
noisy91
environments.
Under
controlled
laboratory
conditions,
participants92
had
to
detect
the
approaching
vehicles.93
2.
Materials
and
methods94
2.1.
Participants95
Eighty-nine
participants
were
recruited
from
educational
and96
social
institutions
(7–86
years
old,
M
=
36.68,
SD
=
22.12).
Split97
into
age
groups,
26
participants
were
juvenile
(19
years
and98
below,
M
=
12.93,
SD
=
2.31),
27
were
early
adults
(20–39
years99
old,
M
=
27.98,
SD
=
5.33),
19
were
middle
adults
(40–59
years100
old,
M
=
50.51,
SD
=
5.94),
and
17
late
adults
(60
years
and
above,101
M
=
71.35,
SD
=
6.96).
To
exclude
prior
major
hearing
deficiency
all102
participants
underwent
audiometric
screening
tests
at
250,
1000103
and
4000
Hz.
As
major
hearing
deficiency
criterion,
late
adults104
all
had
the
1000
Hz
and
4000
Hz
thresholds
under
40
dB
HL.
The105
remaining
participants
had
those
thresholds
under
30
dB
HL.
On
106
average,
children
had
as
thresholds
14,
10,
and
4
dB
HL
at
250,
1000,107
and
4000
Hz
respectively.
At
those
frequencies,
adolescents
had
9,108
5,
and
−1
dB
HL
thresholds;
juvenile
and
early
adults
had
15,
10,109
and
9
dB
HL;
and
late
adults
had
24,
19,
and
21
dB
HL
respectively.
110
Participants
were
all
volunteers.
They
were
instructed
about
the111
general
purpose
of
the
study
and
provided
their
informed
consent112
about
the
participation
in
the
tests
and
the
confidential
data
manip-113
ulation.
Under-aged
participants
had
the
informed
consent
of
their114
caregivers.115
2.2.
Stimuli
and
equipment116
The
pavement
surfaces
selected
for
the
tire-noise
recordings
in117
this
study
were:
cobble
stones,
dense
asphalt,
and
open
graded118
asphalt
rubber.
The
vehicles
were
a
small
passenger
car
(petrol,
119
Volkswagen
Polo),
a
hybrid
(Toyota
Prius),
and
a
pickup
truck120
(diesel,
Mitsubishi
Strakar).
Both
the
representative
sections
of
the121
road
surfaces
and
the
recording
techniques
were
selected
according122
to
the
European
ISO
Standard
11819-1:1997.
The
controlled
pass-
123
by
method
(CPB)
was
used,
with
each
single
vehicle
tire-road
noise124
recorded
with
speeds
of
30,
40
and,
50
km/h.125
The
tire-road
noise
was
binaurally
recorded
with
a
Brüel
&
Kjaer126
Head
and
Torso
Simulator
(HATS)
type
4128-C,
a
Brüel
&
Kjaer
127
Pulse
Analyzer
type
3560-C
and
the
Pulse
CPB
Analysis
software.128
The
noise
samples
were
recorded
with
the
HATS
at
7.5
m
from
the
129
road
centre
and
at
a
height
of
1.7
m
(for
methodological
details
see
130
Freitas
et
al.,
2012).
131
From
each
single
vehicle
recording,
sound
samples
with
the
132
duration
of
2
s
were
produced.
Sound
samples
were
edited
accord-
133
ing
to
a
time-to-passage
(TTP)
criterion.
As
such,
sounds
were
not
134
presented
only
by
vehicle
speed
or
distance
to
the
listener,
but
in
135
a
combined
form
that
is
relevant
for
the
road
user.
The
TTP
for
all
136
stimuli
was
fixed
to
3.5
s
i.e.,
at
the
end
of
the
stimulus
presenta-
137
tion
the
vehicle
would
need
3.5
s
to
cross
the
line
of
sight
of
the
138
observer.
This
TTP
value
is
considered
the
amount
of
time
in
which
139
a
pedestrian
of
any
age
is
able
to
perceive
and
make
an
informed
140
decision
about
crossing
a
road
in
safety.
141
To
mask
the
vehicle
signal,
five
levels
of
white
noise
were
142
generated
with
WaveLab
6:
−40,
−35,
−30,
−25,
and
−20
dBV,
pre-
143
sented
through
the
headphones
at
62,
67,
72,
77,
and
82
dB
(A), 144
respectively.
A
total
of
135
stimuli
with
signal
plus
noise
were
gen- 145
erated
with
audio
software
(Ardour):
3
pavements
×3 vehicles
×
3146
speeds
×
5
noise
levels.
147
The
stimuli
were
presented
through
a
computer
with
a
sound 148
card
Intel
82801BA-ICH2,
a
custom
built
C++
application,
and
AKG
149
K
271
MKII
closed
headphones.
The
C++
application
allowed
the
150
reproduction
of
different
audio
scenes:
multiple
audio
files,
stimuli
151
with
different
number
of
audio
files,
configuration
of
reproduc-
152
tion
time,
configuration
of
the
visual
stimulus
that
appeared
along
153
sound
reproduction,
setting
of
the
sound
pressure
for
each
ear
154
and
collecting
the
participants’
answers.
Using
the
Brüel
&
Kjaer
155
HATS
and
the
Pulse
Analyzer
referred
before,
this
system
was
cali-
156
brated
to
achieve
sound
pressure
levels
identical
to
those
recorded
157
in
the
real
scenarios.
The
values
of
loudness
were
assessed
with
the
158
Psysound3
application
(Cabrera
et
al.,
2008).
159
2.3.
Procedure
160
Within
each
trial,
the
participant
was
presented
with
two
con-
161
secutive
sound
samples,
with
a
fixed
gap
of
1
s,
one
with
the
signal
162
plus
noise
and
the
other
with
only
noise.
Both
noise
backgrounds
of
163
each
trial
had
the
same
level
of
white
noise.
The
135
trials
were
pre-
164
sented
in
a
pseudo-random
order
(method
of
constant
stimulus).
165
Participants
were
requested
to
detect
in
which
of
the
intervals,
i.e.,
166
first
or
second
sample,
was
the
approaching
vehicle
(two-interval
167
forced
choice,
2IFC).
To
avoid
biased
answers
from
participants
the
168
left-right
orientation
of
the
approaching
vehicle
and
the
order
of
169
intervals
were
randomized
across
the
135
trials.
Each
trial
started
170
only
after
an
answer
was
given
to
the
previous
trial,
and
no
time
171
limits
were
imposed.
Therefore,
experiments
did
not
have
a
fixed
172
duration.
173
3.
Results
174
3.1.
Pavement,
vehicle
and
noise
levels
175
A
preliminary
analysis
of
the
data,
after
computing
detection
176
thresholds
per
participant,
revealed
clear
differences
as
a
func-
177
tion
of
age.
The
global
mean
detection
was
of
80.51%
and
the
178
standard
error
(SE)
of
1.09.
The
results
across
age
groups
were:
179
for
juvenile
a
mean
of
78.27%
(SE
=
2.07);
for
early
adults
87.93%
180
(SE
=
1.34);
for
middle
adults
79.84%
(SE
=
1.90);
and
late
adults
181
72.88%
(SE
=
2.33).
These
results
did
differ
significantly
in
a
one
182
way
ANOVA
(F3=
10.95,
p
<
0.001).
In
a
post-hoc
Sheffé
test,
it
was
183
found
that
the
early
and
middle
adults
did
not
differ
significantly 184
(F
=
0.05,
n.s.),
and
neither
did
the
juvenile
differ
from
the
late
185
adults
(F
=
1.2,
n.s.),
but
all
other
age
group
comparisons
remained
186
significantly
different.
Therefore,
average
results
only
represent 187
an
overall
tendency,
and
they
should
be
interpreted
separately
by
188

Please
cite
this
article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
G
Model
AAP
2932
1–7
C.
Mendonc¸
a
et
al.
/
Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx 3
Fig.
1.
Mean
detection
percentages
and
SE
for
pavement,
vehicle
and
noise
(n
=
89).
group age.
As
such,
the
following
analyses
of
detection
thresholds189
per
pavement,
vehicle,
and
noise
are
based
first
on
the
overall
data190
and
then
on
age
group
aggregated
values.191
The
analyses
of
vehicle
detection
as
a
function
of
the
main
vari-
192
ables
(pavement,
vehicle
type,
and
background
noise)
revealed193
clear
trends
as
depicted
in
Fig.
1.
Detection
percentages
increase194
from
the
open
asphalt
rubber
pavement
(M
=
71.87
SE
=
1.25)
to
the195
dense
asphalt
(M
=
76.79,
SE
=
1.25)
and
cobble
stones
(M
=
92.99,196
SE
=
1.16).
Differences
in
vehicle
detection
per
pavement
type
were
197
significant
across
all
age
groups
in
a
two-way
ANOVA
for
repeated198
measures
(F2,3 =
99.84,
p
<
0.001).199
Values
as
a
function
of
vehicle
type
show
a
smoother
but
still200
consistent
trend
from
the
hybrid
(M
=
78.72,
SE
=
1.18)
to
the
small201
passenger
car
(M
=
80.22,
SE
=
1.17)
and
pickup
truck
(M
=
82.56,202
SE
=
1.17).
We
found
a
significant
interaction
between
vehicle203
type
and
detection
across
age
groups
(F3,2 =
14.74,
p
<
0.001),
and204
an
overall
significant
difference
in
detection
level
across
vehi-205
cles
(F2=
3.37,
p
<
0.05),
but
post-hoc
mean
testing
revealed
that206
only
the
hybrid
and
pickup
truck
reached
a
significant
difference 207
(F
=
2.32,
p
<
0.05).
208
For
background
noise,
detection
percentages
were
above
a209
threshold
of
75%
for
the
lowest
three
levels
of
62,
67
and
72
LAeq
(dB
210
A),
with
mean
values
of
91.48
(SE
=
1.24),
88.01
(SE
=
1.38)
and
83.09
211
(SE
=
1.42),
respectively.
Mean
detection
was
close
to
the
threshold
212
for
the
noise
level
of
77
(M
=
74.44,
SE
=
1.34)
and
below
it
for
higher
213
noise
level
of
82
(M
=
65.72,
SE
=
1.04).
214
Detection
as
a
function
of
age
showed
similar
trends
as
the
215
global
ones
described
above
but
with
clear
differences
across
216
groups
(see
Figs.
2–4).
An
inverted
U
pattern
of
results
is
evident,
217
with
participants
from
20
to
39
years
old
reaching
detection
per-
218
centages
above
75%
for
all
but
the
noisiest
background
(M
=
72.04,
219
SE
=
1.54);
while
younger
and
older
participants
perform
clearly
220
worse.
221
Participants
of
60
years
old
and
above
did
not
reach
a
mean
75%
222
threshold
in
several
conditions,
and
in
some
cases,
they
performed
223
close
to
random.
For
the
pavements
of
open
asphalt
rubber
and
224
Fig.
2.
Mean
detection
percentages
and
SE
for
pavements
and
age
groups.

Please
cite
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in
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Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
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Prevention
xxx (2012) xxx–
xxx
Fig. 3.
Mean
detection
percentages
and
SE
for
vehicles
and
age
groups.
dense
asphalt,
the
mean
values
were
of
62.41
(SE
=
2.65)
and
67.94
225
(SE
=
2.20),
respectively.
Detection
means
were
of
69.65
(SE
=
2.57)226
for
the
hybrid
and
of
73.18
(SE
=
2.68)
for
the
small
car.
Impair-227
ment
of
late
adults
was
also
clear
for
the
highest
background
noise228
levels
of
72,
77
and
82
dB(A),
with
mean
detection
values
of
74.47229
(SE
=
2.95),
67.29
(3.11)
and
57.53
(2.22),
respectively.
230
Younger
participants
(7–19
years
old)
performed
slightly
bet-231
ter
than
the
oldest
listeners,
but
clearly
worse
than
early
adults.
In232
addition,
the
detection
ratios
of
juvenile
participants
were
similar233
or
lower
than
those
of
middle-aged
adults
(40–59
years
old).
For
the234
pavements
of
open
asphalt
rubber
and
dense
asphalt,
the
juvenile235
mean
values
were
of
69.85
(SE
=
1.98)
and
75.23
(SE
=
2.20),
respec-236
tively.
Detection
was
barely
above
the
threshold
for
the
hybrid
237
vehicle
(M
=
76.38,
SE
=
1.86).
For
the
highest
noise
levels
of
77
and
238
82
dB(A),
detection
was
below
threshold
with
mean
values
of
72.77239
(SE
=
2.14)
and
63.65
(SE
=
1.77),
respectively.240
Fig.
4.
Mean
detection
percentages
and
SE
for
background
noise
and
age
groups.
As
a
complementary
analysis,
we
selected
some
case
scenarios. 241
We
compared
a
foreseeable
combination
of
low
noise
pavements
242
with
hybrid
cars
against
traditional
pavements
with
conventional 243
passenger
cars.
In
the
first
case,
the
impairment
of
vehicle
detection
244
is
even
clearer
(Fig.
5).
The
overall
mean
detection
in
the
sample
245
is
of
69.60
(SE
=
1.64)
for
the
hybrid/open
asphalt
rubber
condition
246
with
only
the
age
group
of
20–39
years
old
being
able
to
reach
a
247
suprathreshold
of
79.48
(SE
=
2.36).
Older
participants
reached
the
248
lowest
mean
detection
percentage
of
58.41
(SE
=
3.17)
in
that
con-
249
dition
and
they
were
still
below
threshold
for
the
ordinary
vehicles
250
–
dense
asphalt
scenario
with
a
percentage
of
only
70.76
(SE
=
2.55)
251
of
correct
detections.
252
The
combination
of
pavement
and
vehicle
types
seems
to
have 253
an
interactive
effect
on
detection
performance,
which
is
consis-
254
tent
across
age
groups.
Considering
the
overall
data
from
the
255
sample,
the
detection
decreases
4.95%
and
21.15%
from
dense
256
asphalt
and
cobble
stones
to
the
open
asphalt
rubber
pavement,
257
respectively.
The
mean
decrease
is
of
2.67%
from
the
internal
com-
258
bustion
engine
vehicles
to
the
hybrid.
In
the
extreme
scenarios,
the
259
detection
of
the
approaching
vehicles
decreases
8.55%
from
ordi-
260
nary
vehicles/dense
asphalt
to
ordinary
vehicles/cobble
stones.
The
261
highest
difference
is
found
when
comparing
hybrid
vehicles/open
262
asphalt
rubber
with
ordinary
vehicles/cobble
stones,
with
a
23.5%
263
of
decrease
in
detection.
264
Older
adults
of
60
years
old
and
above
are
the
most
impaired
in
265
these
extreme
scenarios
with
detection
differences
of
12.35%
and
266
29.88%.
These
results
point
to
a
somehow
additive
effect
where
267
noisier
vehicles
add
up
to
noisier
pavements
and
interact
with
the
268
listeners’
auditory
accuracy.
269
To
analyze
this
interactive
effect,
we
addressed
the
detection
270
of
incoming
traffic
in
light
of
a
signal-to-noise
perspective.
We
271
focused
on
the
ratio
between
the
actual
traffic
signal
and
the
back-
272
ground
noise,
aiming
to
find
the
relation
between
this
ratio
and
273
vehicle
detection.
274
A
systematic
preliminary
analysis
compared
several
equations
275
to
compute
the
signal-to-noise
ratio
(SNR),
namely:
SNR
=
S/N, 276
SNR
=
S
−N,
SNR
=
fS/N,
and
SNR
=
fS
−
N,
where
S
stands
for
sig-
277
nal,
fS
stands
for
final
signal
(last
250
ms
of
the
stimulus),
and
N
278
stands
for
noise.
All
these
analyses
were
performed
both
for
the
279

Please
cite
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article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
G
Model
AAP
2932
1–7
C.
Mendonc¸
a
et
al.
/
Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx 5
Fig.
5.
Overall
and
age
groups
mean
detection
percentages
and
SE
for
selected
scenarios.
median
and
for
the
maximum
values
of
the
sound
samples.
From
280
all
acoustic
measures,
the
best
detection
prediction
was
obtained281
by
SNR
=
fS/N.
Both
median
and
maximum
values
yielded
similar
282
results.283
Comparing
all
acoustic
indicators,
we
found
that
the
best
mea-284
sure
was
loudness,
when
compared
to
LAeq
and
LAmax.
There
were285
no
interactions
with
Sharpness
or
with
Roughness
indicators.
The286
scatter
plot
of
all
loudness
SNRs
against
detection
accuracy
levels287
is
presented
in
Fig.
6.288
Several
data
fitting
methods
were
tested
to
predict
how
both
289
the
SNR
and
the
detection
levels
interact,
namely
linear,
polynomial290
and
logistic
fits.
The
best
correlation
was
obtained
with
a
logistic
fit291
(r2=
0.77,
p
<
0.001),
despite
significant
correlations
with
the
poly-292
nomial
and
linear
fits
(r2=
0.34
and
r2=
0.67,
respectively).
LAeq
SNR293
did
not
reach
statistically
significant
fits,
while
LAmax
SNR
only
cor-294
related
marginally
with
detection
(linear
fit:
r2=
0.32;
polynomial295
fit:
r2=
0.37;
logistic
fit:
r2=
0.38).296
To
analyze
how
pavement
and
vehicle
type
affected
the
SNR297
detection
values,
we
plotted
each
variable
separately
(see
Figs.
7298
and
8).
As
observed
in
Fig.
7,
vehicle
type
is
well
distributed
across
299
Fig.
6.
The
signal-to-noise
ratio
[dB
(A)]
against
detection
levels
and
logistic
fit.
Fig. 7.
The
signal-to-noise
ratio
against
detection
levels,
separating
the
vehicle
vari-
able.
Fig.
8.
The
signal-to-noise
ratio
against
detection
levels,
separating
the
pavement
variable.

Please
cite
this
article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
G
Model
AAP
2932
1–7
6C.
Mendonc¸
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et
al.
/
Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx
the
plot.
There
is,
however,
a
clear
distinguished
pavement
pattern,300
as
seen
in
Fig.
8.301
Curve
fitting
for
each
separate
pavement
variable
was
per-302
formed.
We
found
a
significant
linear
correlation
between
the
open303
asphalt
rubber
pavement
(r2=
0.79,
p
<
0.001)
and
the
dense
asphalt304
(r2=
0.75,
p
<
0.001),
but
not
for
the
cobble
stones
(r2=
0.22,
n.s.).305
It
was
the
logistic
fit
that
provided
the
best
result
prediction
for306
each
variable,
as
seen
in
Fig.
8,
open
asphalt
rubber
obtaining
a307
correlation
of
r2=
0.86
(p
<
0.001)
and
dense
asphalt
of
r2=
0.83308
(p
<
0.001).
Again,
there
was
no
significant
interaction
between
the309
cobble
stones
pavement
and
the
detection
levels
with
the
logistic310
fitting
procedure.
This
result
highlights
the
specific
properties
each311
pavement
type
and
suggests
that
each
variable
should
be
analyzed312
separately.
313
4.
Discussion314
Our
results
clearly
show
a
negative
impact
of
traffic
noise315
abatement
on
the
detection
of
approaching
vehicles.
Detection
is316
significantly
lowered
by
low
noise
pavements
and
quieter
vehicles.317
Interestingly,
pavement
type
had
a
stronger
effect
than
vehicle
type
318
on
the
detection
levels.
This
might
reveal
that
tire-road
noise
is
a319
more
relevant
cue
for
vehicle
detection
than
engine
noise,
namely320
at
lower
traffic
velocities
such
as
those
used
in
this
study.
The
anal-321
ysis
of
this
finding
may
become
very
complex
since
tire-road
noise322
at
low
speeds
is
influenced
not
only
by
the
type
of
road
surface323
but
also
by
tire
characteristics
such
as
pattern
and
wear.
In
this324
study,
the
tires
of
the
hybrid
vehicle
were
nearly
new,
therefore325
less
noisy,
while
the
tires
of
the
other
vehicles
were
worn.
In
this326
way
the
effect
of
the
vehicle
type
was
clearly
differentiated.327
This
finding
should
be
taken
into
consideration
in
future
stud-
328
ies
on
traffic
noise
abatement.
Indeed,
there
is
barely
any
research329
related
to
pavement
type
and
specifically
low
noise
pavements,330
when
compared
to
the
high
data
volume
on
vehicle
engines.
Our331
data
strongly
suggest
that
different
asphalt
mixtures
will
con-332
tribute
differently
to
traffic
conspicuity
and
vulnerable
road
users’333
safety.
Furthermore,
a
novel
approach
to
tire
effect
on
detection334
should
be
carried
out.335
Also,
age
is
a
critical
factor.
Younger
and
particularly
older
par-
336
ticipants
are
the
most
impaired.
The
worse
detectability
levels
in
337
older
listeners
most
likely
reflect
the
typical
hearing
loss
associated338
with
age.
On
the
other
hand,
the
decreased
detectability
in
younger339
groups
is
congruent
with
data
pointing
out
that
as
they
grow
older,340
children
increase
their
accuracy
in
vehicle
identification
(Pfeffer341
and
Barnecutt,
1996).342
Not
only
the
variables
revealed
direct
and
separate
effects
on
343
the
vehicle
detectability,
but
they
also
showed
interactive
effects.344
This
fact
points
out
to
the
need
for
comprehensive
approaches345
that
account
for
subject’s
age
(or
listening
abilities),
vehicle
and346
pavement
type,
as
well
as
background
noise.
These
interactions347
might
be
regarded
as
a
result
of
loudness
additivity.
Noisier
cars348
and
pavements
should
be
more
accurately
identified
by
better
lis-349
tening
groups.
Loudness
signal-to-noise
ratio
did
indeed
reveal
350
some
predictability,
but
it
did
not
account
for
all
variables.
Cob-351
ble
stones
traffic
sounds
remain
highly
detected
despite
varying352
loudness
levels,
probably
due
to
their
spectral
or
rhythmic
patterns.353
Nevertheless,
the
finding
that
loudness
is
the
best
acoustic
measure354
to
predict
vehicle
detection,
against
LAeq
and
LAmax,
is
consistent355
with
our
previous
results,
pointing
to
loudness
as
the
best
predictor356
of
traffic-noise
annoyance
(Freitas
et
al.,
2012),
and
brings
further
357
support
to
the
claim
that
environmental
noise
assessment
should
358
have
this
measure
as
a
standard.
359
One
major
concern
standing
out
from
this
study
relies
on
some360
age
groups
(younger
and
older
participants)
performing
below361
a
threshold
of
75%,
or
even
close
to
random,
in
several
traffic
362
scenarios.363
In
the
real
world,
the
detection
performance
is
likely
to
be
364
even
worse.
On
the
one
hand,
we
used
a
standard
white
noise
365
background,
while
in
everyday
situations
road
traffic
contributes
366
heavily
to
the
noise
environment,
thus
reducing
the
conspicuity
of
367
the
sound
envelope
of
each
vehicle.
On
the
other
hand,
in
our
exper-
368
iments,
participants
only
had
to
detect
one
approaching
vehicle
at
a
369
time
instead
of
simultaneously
facing
several
targets,
which
would
370
be
the
case
in
common
urban
scenarios.
371
Moreover,
transition
periods
between
vehicle
or
pavement
type
372
are
potentially
very
difficult
and
risky.
Vulnerable
road
users
will
373
inevitably
have
to
cope
with
a
growing
mix
of
vehicles
and
pave-
374
ments,
with
varying
degrees
of
conspicuity.
In
such
a
transition
375
scenario,
hybrid
and
all-electric
vehicles,
circulating
on
low
noise
376
pavements,
might
prove
quite
difficult
to
detect.
Therefore,
a
trade-
377
off
between
a
more
pleasant
and
healthy
urban
road
environment
378
and
an
increase
of
traffic
conflicts
and
accidents
involving
pedestri- 379
ans
and
bicyclists
should
be
a
matter
of
concern.
In
the
next
section 380
we
approach
this
matter
from
the
decision-maker
point
of
view. 381
5.
Trade-off
analysis
382
From
the
data
here
presented
a
new
debate
should
start
on
noise,
383
urban
design
and
traffic
policy
to
account
for
the
benefits
and
dan-
384
gers
of
traffic
sounds.
Here
we
present
an
analysis
of
this
trade-off
385
accounting
for
the
vehicle
detection
levels
above
discussed
and
386
overall
traffic
annoyance
levels
found
in
another
paper
(Freitas
387
et
al.,
2012).
388
The
critical
factors
to
consider
in
the
decision
making
process
389
are:
pedestrians’
age,
vehicle
type,
pavement
type,
environmental
390
noise
and
traffic
speed.
391
i. Pedestrians’
age:
Age
is
a
critical
factor
in
vehicle
auditory
392
detection,
but
not
in
traffic
sound
annoyance
ratings.
Therefore,
393
vulnerable
road
users,
such
as
children
and
older
adults
detect
394
less
efficiently
vehicles
in
noisy
environments,
but
they
are
still
395
as
affected
by
noise
as
other
age
groups.
On
the
other
hand, 396
using
age
as
a
criterion
for
traffic
noise
management
would
397
be
difficult
in
practice.
Planning
urban
design
by
finding
the
398
most
frequent
target
users
would
require
surveys
or
population
399
studies,
which
would
not
be
feasible
in
most
cases.
The
simple
400
solution
of
introducing
elements
that
create
loud
sounds
(such
401
as
cobble
stone
pavements)
at
obvious
sites
such
as
around
402
schools
is
also
to
be
avoided,
as
traffic
noise
significantly
affects
403
children
leaning
(Sanz
et
al.,
1993).
From
this
data,
it
stands
out
404
that
specifically
addressing
these
road
users
by
introducing
loud
405
elements
is
not
the
best
approach,
as
the
annoyance
trade-off
406
would
emerge.
407
ii.
Vehicle
type:
Vehicle
type
affects
both
detection
levels
and
408
annoyance
ratings
in
a
symmetrical
way.
Louder
engines
are
409
more
detected
and
more
annoying,
while
quieter
engines
are
410
less
detected
and
less
annoying.
Therefore,
from
the
trade-off
411
point
of
view,
there
is
no
optimal
solution
regarding
vehicle
412
type.
Some
solutions
have
been
pointed
out,
such
as
introducing
413
sound
in
quieter
vehicles,
but
such
solutions
would
reintroduce
414
some
environmental
noise,
with
unpredictable
psychological
415
reactions.
While
these
solutions
are
still
under
debate,
and
are
416
therefore
of
difficult
application
and
analysis,
other
approaches
417
should
be
sought.
418
iii.
Pavement
type:
Interestingly,
this
factor
is
more
crucial
than
419
vehicle
type
in
lower
speed
traffic
environments,
such
as
those
420
where
pedestrians
might
be
involved.
Coble
stone
pavements
421
are
significantly
more
annoying
than
other
pavement
types. 422
They
also
provide
the
best
detectability
levels,
being
always
423
above
85%
on
average.
Dense
asphalt
yields
greater
detectability
424
than
asphalt
rubber,
while
not
being
significantly
more
annoy- 425
ing.
As
such,
cobble
stone
pavements
are
an
efficient
measure
426

Please
cite
this
article
in
press
as:
Mendonc¸
a,
C.,
et
al.,
Noise
abatement
and
traffic
safety:
The
trade-off
of
quieter
engines
and
pavements
on
vehicle
detection.
Accid.
Anal.
Prev.
(2012),
http://dx.doi.org/10.1016/j.aap.2012.10.018
ARTICLE IN PRESS
G
Model
AAP
2932
1–7
C.
Mendonc¸
a
et
al.
/
Accident
Analysis
and
Prevention
xxx (2012) xxx–
xxx 7
to
ensure
that
even
in
worst
traffic/environment
combinations427
road
users
will
detect
incoming
vehicles.
On
the
other
hand,428
when
no
specific
traffic
safety
concern
exists,
dense
asphalt429
pavements
should
be
preferred
due
to
their
higher
detectability430
with
no
annoyance
trade-off.431
iv.
Environmental
noise:
This
critical
factor
influences
both
annoy-432
ance
and
vehicle
detection
and
it
is
affected
by
pavement,
speed,433
and
vehicle
type.
Average
detectability
results
reveal
that
with434
environmental
noise
at
77
dB
(A)
detections
are
close
to
75%435
threshold.
Louder
environments
will
be
below
threshold,
and436
therefore
more
dangerous.
More
silent
environments
will
be437
safer
in
general.
As
such,
environmental
noise
should
be
taken438
as
a
decision
criterion.
Louder
environments
should
have
cob-439
ble
stone
pavements;
and
silent
environments
(under
77
dB
A)440
should
have
asphalt
pavements.
As
such,
in
silent
environments,441
annoyance
would
be
prevented
while
preserving
safety.
In
loud442
environments,
safety
would
come
first.443
v. Traffic
speed:
This
factor
was
not
considered
in
this
study,444
as
it
interacts
with
the
other
elements,
such
as
vehicle
dis-445
tance.
Faster
vehicles
will
generate
more
noise,
and
therefore446
better
identified,
but
they
will
become
more
dangerous.
The
447
opposite
is
observed
with
slower
vehicles.
But
velocity
strongly448
affects
annoyance
ratings.
Both
dense
asphalt
and
asphalt
rub-449
ber
become
more
annoying
with
higher
traffic
speed,
but
even450
at
70
km/h
they
will
not
be
as
annoying
as
a
cobble
stone
pave-451
ment
at
only
30
km/h.
Cobble
stone
pavements
also
become452
more
annoying
with
higher
speed,
achieving
80%
of
annoyance453
at
50
km/h.
Taking
such
data
into
account,
lower
traffic
speeds454
should
always
be
promoted,
namely
in
residential
and
work455
areas.
Also,
lower
traffic
speeds
should
be
enforced
in
all
cobble456
stone
pavement
areas.
457
In
conclusion,
from
our
trade-off
analysis,
we
propose
a
simple458
decision
model
for
urban
designers.
Environmental
noise
should459
always
be
assessed
at
the
planning
phase.
If
under
77
dB
A,
asphalt460
pavements
are
preferred.
If
higher,
cobble
stones
pavements
will461
better
promote
traffic
safety,
but
additional
measures
to
control462
traffic
speed
should
be
envisaged.
Naturally,
most
environmen-463
tal
noise
is
indeed
influenced
by
traffic
noise
namely
in
urban
464
areas.
Therefore,
this
decision
model
should
be
carefully
consid-
465
ered,
accounting
for
the
predicted
environmental
noise
after
urban
and
traffic
changes.
Additional
studies
should
test
and
validate
our
466
assumptions,
and
other
potentially
relevant
parameters
should
be
467
addressed.
468
Acknowledgements
469
This
study
was
financed
by
the
Portuguese
Foundation
470
for
Science
and
Technology
and
FEDER,
projects
FCOMP-
471
01-0124-FEDER-007560,
FCOMP-01-0124-FEDER-022674,
PEst-
472
OE/ECI/UI4047/2011,
and
PTDC/TRA/67859/2006.
We
thank
Prof.
473
Pedro
Arezes
for
his
technical
support.
474
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