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```1
MILAN DYNAMIC NOISE MAPPING FROM
FEW MONITORING STATIONS:
STATISTICAL ANALYSIS ON ROAD NETWORK
Giovanni Zambon, Roberto Benocci, Alessandro Bisceglie,
H. Eduardo Roman
Dipartimento di Scienze dell’Ambiente e del Territorio e di Scienze della Terra,
University of Milano – Bicocca, Italy
2
1. DESCRIPTION OF THE URBAN NETWORK
2. DATA COLLECTION

Acoustic data

Processing of the acoustic measurements

Production of a Geodatabase
3
The Urban Network
AREA TO BE MAPPED:
Milan Pilot Area
belonging to all different
types of urban roads:
•
•
•
4
Campaign of acoustic monitoring of road traffic noise in city of Milan
Collection of temporal trends of noise levels in order to create a significant sample for a
statistical analysis
Steps:
•
•
•
•
•
•
•
collection and selection of previous noise data
selection of monitoring sites based on specific criteria
acquisition of the acoustic data
correlation of acoustic data with weather data
identification and deletion of abnormal events
acquisition of series of equivalent sound levels
storing data in a GEODATABASE
5
RESULTS OF NOISE MONITORING CAMPAIGNS
The patterns of traffic flows (and therefore of noise) are very regular and repetitive.
The streets can be joined together in a limited number of groups (clusters).
6
3. STATISTICAL ANALISYS OF THE MONITORED STRETCHES

Acoustic Database

Cluster analysis

Comparative analysis among profiles of different temporal
discretization

Error Analysis at Different Temporal Discretization

Error Analysis for different cluster composition
7
Parameter used for roads comparison
Each i-th value of the temporal series was referred to the corresponding daytime
LAeqdj (06-22 h) taken as reference level, that is for each hour the following
parameter ᵟij was computed:
 ij  LAeqh  LAeqdj
[dB] (j= 1, ………., 93)
75
5
70
0
65
-5
60
-10
Absolute LAeqh
Day-time LAeqd
55
-15
LAeqh referred to LAeqd
50
-20
6
8
10 12 14 16 18 20 22 24
Time [hours]
2
4
h = LAeqh - LAeqd [dB]
Hourly equivalent level LAeqh [dB(A)]
ij
Example of the normalization of the
24 h profile of the hourly LAeqh
8
Cluster analysis of hourly noise profile
By means of a cluster analysis we
obtain two clusters clearly distinct
MDS Cluster
The Multi-Dimensional Scaling (MDS) applied
to the data provides a visual representation of
the pattern of proximities among the data. The
distinction among clusters, marked by different
colors, is rather good.
9
Distribution of the different road classes D,E,F in the two clusters
Hourly
cluster
Total
D
E
F
5
12
39
Cluster 1
56
(9%)
(21%)
(70%)
4
20
13
Cluster 2
Total
37
(24%)
(54%)
(35%)
9
32
52
93
10
Comparison among mean profiles with different temporal discretization
Good stability of noise profiles for all temporal resolution
11
Error Analysis at Different Temporal Discretization
Standard deviations as a function of daily time for the different integration intervals for all arches
considered for cluster 1 and 2
High variability of standard deviations
12
Cluster 1
Cluster 2
Updating time as a function of daily period
5
Time Interval
07h 07h-21h
- 21h
Time Interval
5 min
Mean standard deviations, σ, [dB] as a function of
integration time (for the different time intervals)
[dB]
4
3
2
1
0
0
10
20
30
40
50
60
Integration Time [m]
Cluster 1
Cluster 2
Cluster 1
Cluster 2
5
5
Time Interval
Time Interval
21h -21h-01h
01h
4
 [dB]
4
 [dB]
Time Interval
01h01h- 07h
Time Interval
07h
15 min
3
3
2
2
1
1
0
0
0
10
20
30
40
Integration Time [m]
50
60
0
10
20
30
40
Integration Time [m]
50
60
60 min
13
4. AGGREGATION OF THE ROAD STRETCHES

The non-acoustic parameter (x)

Determination of the optimal non-acoustic parameter
 Receiver Operating Characteristic (ROC) analysis

Interpolation method between the two mean noise behaviors

Discretization of the parameter x

Identification of monitoring network sites inside Zone 9
14
AGGREGATION OF THE ROAD STRETCHES
Roads stretches of the whole pilot area have to be grouped together when
they display a similar traffic noise behavior.
One group
One basic noise map
(Afterwards we’ll see that starting from cluster analysis results (2 cluters), we
can interpolate the behavior of noise of every stretch between the two regimes)
Road traffic noise is not known, so we look at the corresponding values of
hourly traffic flows, which are known for all stretches in the urban area:
Determination of an optimal non-acoustic parameter
X=
15
Determination of the optimal non-acoustic parameter
The optimal non-acoustic parameter is the one that best discriminates the two clusters objects.
The reliability of non-acoustic parameters was assessed by ROC (Receiver Operating Characteristic) curves, a graphical
method to evaluate the performance of a binary classifier. The curve is created by plotting the true positive rate (TPR)
against the false positive rate (FPR) at various threshold settings. The index related to the Area Under the Curve (AUC)
is equivalent to the probability that the result of the test on a group of roads with non-acoustic parameter over the
threshold belongs to the proper cluster.
In table, the threshold values and the correspondent AUC are reported for each non-acoustic parameter considered.
Non-Acoustic Parameter
Threshold Value
log Tt
4.45
Area Under the Curve (AUC)
(%)
79.6
log Td
4.42
79.1
log Tn
3.21
81.3
(log F8−9 )2 + (log F21−22 )2
4.16
78.9
(log F8−9 )2 + (log F18−19 )2
4.69
77.5
(log F8−9 )2 + (log F3−4 )2
3.65
79.2
(log F8−9 )2 + (log F21−22 )2 + (log Tt )2
6.20
79.1
(log F8−9 )2 + (log Tn )2
4.47
80.1
(log Td )2 + (log Tn )2
5.30
80.8
(log Td )2 − (log Tn )2
F8−9
1.02
72.1
2007
76.2
16
Determination of the optimal non-acoustic parameter
The best result is obtained for LogTN and its combinations.
However, due to the uncertainty of the night flow rate calculated by the traffic model,
we opted for LogTT.
ROC curve for the non-acoustic parameter
LogTT. The AUC of 79.6% is reported
together its 95% confidence level interval.
17
Results of the aggregation for the non-acoustic parameter
X=LogTT
Frequency distribution of parameter x
(histogram) and corresponding
probability functions for the sample of
monitored roads, inside Cluster 1 and 2
Probability distribution functions P(x) for Cluster 1 and 2, for the parameter: X = Log TT
We observe a large superposition of the two cluster
distributions P1(x) and P2(x)
18
Interpolation method between two mean noise behaviors
linear combination between the two trends of the traffic noise from each cluster, for the given value of x.
The weights (α1, α2) of the linear combination can be obtained for each value of x using the relations: α1=P1(x) and
α2=P2(x). That is, for a given value of x we find the ‘components’ α1 and α2 from the analytical expressions of P(x).
The values of α1,2 represent the ‘probability’ that a given road characterized by its own value of x belongs to the
Cluster 1 and 2.
So the resulting hourly behavior of the road noise is a linear combination of the mean noises (noise ∆) measured for
Cluster 1 and 2, denoted respectively as δC1(h) and δC2(h). The predicted traffic noise behavior, δpred(h), of a given value
of x is then obtained by normalizing the values of α1,2 denoted as β:
Using the values of β we can predict the hourly variations δ(h) for a given value of x according to:
δpred(h) = β1* δC1(h) + β2* δC2(h)
The error made in using previous eq. can be estimated by calculating the standard deviation ε of the prediction
δpred(h) from the measured values δmeas(h), that is:
19
Aggregation of the road stretches for the selected non-acoustic parameter
X=LogTT
Parameter X = LogTT has been calculated for all the road stretches within Milan pilot area (about 2200 roads).
Their distribution is compared with the probability distribution function P(x) obtained from the measurements at
the 93 noise recording stations
Frequency distribution of parameter x
(histogram) for the whole pilot area roads
and probability functions (blue line) for
the sample of monitored roads
20
Aggregation of the road stretches for the selected non-acoustic parameter
X=LogTT
From this distribution we have chosen 6 equally populated intervals of X (cake graphic).
To each group of X values a noise map will be associated and will be updated in real time by
measuring the noise trend in few selected locations for the roads inside each group.
Mean values of β for the six groups of x = log TT.
Range of X
0,0 – 3,0
3,0 - 3,5
3,5 - 3,9
3,9 - 4,2
4,2 - 4,5
4,5- 5,2
β1
0,99
0,81
0,63
0,50
0,41
0,16
β2
0,01
0,19
0,37
0,50
0,59
0,84
These mean values of β can be used according to equation:
δpred(h) = β1* δC1(h) + β2* δC2(h)
in predicting the traffic noise associated to roads belonging to a
given group.
21
Aggregation of the road stretches for the selected non-acoustic parameter
X=LogTT
Procedure to select the locations of the noise recording stations inside each one of the six groups of
x. The choice is based on the use of the hourly flows taken from the traffic simulation model. To do that,
we calculate the hourly mean traffic flow inside each group
Inside each group, we calculate the mean
standard deviation of each road flow with
respect to its mean δ.
The following list of roads is sorted from the
closest distance to the mean (top road) till the
20th distance
This graph shows the mean hourly variations of the flow for each
of the 6 groups, normalized using the rush hour values (08-09)
22
Identification of monitoring network sites inside Zone 9 (Milan pilot area)
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6
0,0-3,0
δ
3,0-3,5
δ
3,5-3,9
δ
3,9-4,2
δ
4,2-4,5
δ
4,5-5,2
δ
10531:10532
0,24
2168:10517
0,23
9247:9342
0,17
3103:8808
0,09
12039:16188
0,06
Stelvio_H
0,06
12093:13727
0,25
2953:11972
0,23
11845:11847
0,19
11927:12023
0,10
3121:16188
0,06
11985:11986
0,06
11966:11967
0,30
2953:3124
0,23
12161:12193
0,19
8808:11926
0,11
6974:12039
0,06
Murat_A
0,06
11966:33166
0,30
2158:13703
0,24
12167:12193
0,19
0,11
3064:8774
0,09
2160:11984
0,06
12092:13727
0,32
3154:11639
0,25
12157:12194
0,19
3103:11927
0,11
8774:12026
0,09
11984:11985
0,07
3029:3042
0,33
3165:11995
0,26
12161:12194
0,19
3132:11965
0,12
6974:12008
0,09
Zara_K
0,07
20540:31375
0,33
23024:23028
0,27
3939:13730
0,20
11926:11965
0,12
12994:13767
0,09
Zara_L
0,07
3965:3968
0,33
23028:23033
0,27
2479:31157
0,20
Pirelli_C
0,12
3168:12008
0,09
Zara_M
0,07
2172:27405
0,34
23035:23036
0,27
2103:8286
0,20
Pirelli_D
0,12
3031:3907
0,09
Sauro_A
0,07
27405:31375
0,34
3668:23035
0,27
2116:9353
0,21
Emanueli_D
0,12
2108:27223
0,09
Zara_D
0,07
3397:12121
0,34
2769:12083
0,27
12035:16179
0,21
Emanueli_E
0,12
2141:27223
0,09
Zara_C
0,07
4196:12121
0,34
2527:23161
0,27
16178:16179
0,21
3931:10314
0,12
12026:16189
0,10
Zara_E
0,07
5049:5051
0,34
2527:2554
0,27
2156:8286
0,21
3094:12023
0,13
3121:16189
0,10
Zara_N
0,07
34391:34392
0,35
11968:11969
0,27
3921:4065
0,21
10313:10314
0,13
2093:31418
0,10
Zara_O
0,07
34392:34393
0,35
3934:13721
0,28
27227:31381
0,21
3095:3096
0,14
3064:12024
0,10
Zara_B
0,08
3032:3041
0,36
3935:13721
0,28
27227:31383
0,21
3095:3102
0,14
12145:12146
0,10
Istria_A
0,08
3032:6031
0,36
2538:23161
0,28
2083:2162
0,21
3172:3173
0,14
12146:12147
0,10
Gioia_M
0,08
3042:6032
0,36
5049:13710
0,28
12103:16003
0,23
3173:13822
0,14
Pasta_A
0,10
Fermi_AA
0,08
6031:6032
0,36
16019:23024
0,28
12104:16003
0,23
Cozzi_G
0,14
Girardengo_B
0,10
Fermi_AB
0,08
3382:12017
0,36
5047:16019
0,28
11887:31396
0,23
10322:16000
0,15
Girardengo_C
0,10
Fermi_Z
0,08
23
Milan Pilot Area
Top 20 locations
𝑥 = log 𝑇𝑇
LEGEND
0,0 - 3,0
3,0 - 3,5
3,5 - 3,9
3,9 - 4,2
4,2 - 4,5
4,5- 5,2
DYNAMAP Noise
Monitoring Points
Pilot area
24
Pilot Area (~2200 roads)
Acoustic dataset (~100 roads)
Two clusters
Cluster analysis
6 groups n
Distribution
analysis
δC1(τ) , δC2(τ) over 24 hours
τ = 5, 15,…, 60 min
Non-acoustic
parameter X
δpred(τ) = β1* δC1(τ) + β2* δC2(τ)
β1 ,β2 = parameters of probability
function
Mean Xn ,corresponding
values of β1(n) and β2(n)
Low cost terminals measures
(assigned to two clusters (C1’, C2’)
analysing starting data recordings)
6 basic
noise maps
Noise calculation:
Noise updating for
each point of map n
δpred(τ) = β1* δC1’(τ) + β2* δC2’(τ)
Sum of updated maps
```
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