WCTRS SIG G3 – Urban Transport Planning and Policy – “ Climate Change Targets and Urban Transport Policy”
13 – 14 April 2015, University of Malta Valletta Campus
THE FORECASTING OF ROADSIDE POLLUTANT
LEVELS TO EVALUATE TRAFFIC MANAGEMENT
MEASURES IN PALERMO
Francesco Castelluccio, Mario Catalano, Marco Migliore
University of Palermo, Department of Civil, Environmental, Aerospace and Materials
Engineering (DICAM), Transport Research Group
OUTLINE
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Research question & Literature
Models: Neural Networks
Study area
Model determinants
Neural Network response graphs
Neural Networks results: observed versus predicted
Cunclusions & Future steps
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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RESEARCH QUESTION & LITERATURE
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How can air pollution from traffic be forecasted in order to
support sustainable urban transport policies (traffic
management)?
Few studies have been carried out to forecast the concentrations
near urban arterials (Moseholm et al. ,1996; Dorzdowicz et al.,
1997; Perez and Trier, 2001; Viotti et al., 2002; Nagendra and
Khare , 2006; Ming Cai et al., 2009).
In Galatioto, Migliore and Zito (2009) the neural networks have
been used to predict the CO and C6H6 in the urban area of
Palermo and a comparative analysis among the different traffic
parameters (flow, queue length, occupancy degree and travel
time) was carried out by neural networks. The analysis highlighted
that the queue length was the better correlated traffic parameter
to the pollutant concentrations.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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RESEARCH QUESTION & LITERATURE
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Moseholm, L., Silva, J., Larson, T.C., 1996. Forecasting carbon monoxide
concentration near a sheltered intersection using video traffic surveillance
and neural networks. Transportation Research Part D 1, 15–28.
Dorzdowicz, B., Benz, S. J., et. al., 1997. A neural network based model for
the analysis of carbon monoxide concentration in the urban area of
Rosario, Computational Mechanics Publications, Southampton, 677-685.
Perez, P., Trier, A., 2001. Prediction of NO and NO2 concentrations near a
street with heavy traffic in Santiago, Chile. Atmospheric Environment 35,
1783–1789.
Viotti, P., Liuti, G., Genova, P.D., 2002. Atmospheric urban pollution:
applications of an artificial neural network (ANN) to the city of Perugia.
Ecological Modelling 148, 27–46.
Ming, Cai , Yafeng, Yin , Min, Xi, 2009. Prediction of hourly air pollutant
concentrations near urban arterials. Transportation Research Part D 14,
32–41.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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RESEARCH QUESTION & LITERATURE
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Many variables influence roadside pollutant levels:
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different vehicle typologies
engine’s temperature
maintenance level of engines
antipollution devices
different cinematic conditions
urbanistic structure of the site
weather conditions...
 Estimation of roadside pollutant levels:
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Parametric statistical models
Neural networks (you do not have to suppose the non-linear
function between outputs and inputs)
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: THEORY
Neural Network
x0 = 1
f a j  
w0 = - bj
xn
... ...
xk
Neuron j
wk
wn
S
1
1
1  exp( a j )
y
aj
y
Activation
aj
Neural networks are composed of many
simple elements operating in parallel, taking
inspiration from biological nervous systems.
 n

 n

y  f (a j )  f   wk xk  b j   f   wk xk 
 k 1

 k 0

“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: THEORY
Multilayered feed-forward Neural Network
A neural network can be trained therefore
to perform a particular function by
adjusting the values of the connections
between elements.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: THEORY
NEURAL NETWORKS ARE UNIVERSAL APPROXIMATORS
The two-layer sigmoid/linear (as activation functions for neurones in each layer) network can
represent any functional relationship between inputs and outputs if the sigmoid layer has
enough neurones (Hornik, 1989)
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: THEORY
E(u)
OVERFITTING
Test set
E(u*)
Validation set
Training set
u*
Training step u
During the training of the network is important to compare the forecast performance on a test set to
exclude an over fitting to the training set and, then, to test the capability of the network to
generalize the results (Bishop, 1995).
The data set should be partitioned randomly into a training set and a test set. The training set should
be further partitioned into two subsets: a subset used for the estimation of the model (training) and
a subset used for the evaluation of the performance of the model (validation data set for the early
stopping method). The optimal structure can be found pruning step by step the neurons of the
hidden layer until to minimize the MSE function on the validation data set.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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STUDY AREA
PDU
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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STUDY AREA
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Pollutants Detection Unit – Castelnuovo square:
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It is surrounded by three heavily trafficked one way roads:
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The pollutant detection unit contain:
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Libertà boulevard with a medium traffic of 1400 vehicles per hour,
Emerico Amari street with a medium traffic of 1800 vehicles per hour,
Dante Alighieri street with a medium traffic of 1000 vehicles per hour.
two particulate matter analyzers (PM10; PM2,5),
conventional analyzers (NO, NO2; NOx; CO; COx; SO2),
meteorological weather measures (solar radiation, rainfall level, wind speed and direction,
humidity).
It appears that from 2008 the pollution problems are similar each
year, such as the exceeding of the threshold levels regarding PM10
and the annual average NO2.
it was carried out the reconstruction of the monthly average week
for 12 months.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODEL DETERMINANTS
 Dependent variable: hourly mean concentration of nitrogen
bioxide, NO2, in µg/m3
 Explanatory variables: NO2 hourly mean concentration 1
hour before (µg/m3), CO hourly mean concentration
(mg/m3), wind speed (m/s), Solar radiation (W/m2), Vertical
Gradient of Temperature (°C/m);
 Modelling approach: artificial intelligence (neural network).
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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THE CORRELATION BETWEEN THE CO POLLUTANT
CONCENTRATION AND THE VEHICULAR FLOW
Sept-Nov
6,000
2.000
1.800
5,000
1.600
1.400
4,000
1.200
3,000
1.000
0.800
2,000
0.600
0.400
1,000
0.200
0
0.000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71
Traffic
In order to take into account the vehicular traffic variable, CO pollutant
concentrations have been used as proxy variable for forecasting NO2 pollutant
concentrations. In fact the detection unit of RAP measures the CO levels
continuously.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK ARCHITECTURE (using Levenberg Marquardt learning algorithm and the pruning approach)
INPUT
LAYER
OUTPUT
LAYER
NO2t-1
COt
Wind speedt
NO2t
Solar radiationt
Vertical Gradient of Temp.t
HIDDEN
LAYER
Training
Performance
Selection
Performance
Test
Performance
Obs.-Pred.
Correlation
0.418
0.396
0.420
0.91
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK RESPONSE GRAPH:
correlation between NO2 and NO2 of the previous hour
Response Graph, NO2 (10 )
95
90
85
80
75
70
NO2
65
60
55
50
45
40
35
30
25
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
Model
NO2.10
NO2-1
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK RESPONSE GRAPH:
correlation between NO2 and CO
Response Graph, NO2 (10 )
90
85
80
75
NO2
70
65
60
55
50
45
40
35
-1,0 -0,5 0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
4,0
4,5
5,0
5,5
6,0
6,5
Model
NO2.10
CO
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK RESPONSE GRAPH: correlation between
NO2 and wind speed
Response Graph, NO2 (10 )
53
52
51
50
49
NO2
48
47
46
45
44
43
42
41
-0,2
0,2
0,0
0,6
0,4
1,0
0,8
1,4
1,2
1,8
1,6
2,2
2,0
2,6
2,4
3,0
2,8
3,4
3,2
3,8
3,6
Model
NO2.10
Vvento
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK RESPONSE GRAPH: correlation between
NO2 and solar radiation
Response Graph, NO2 (10 )
49
48
47
46
45
44
NO2
43
42
41
40
39
38
37
36
35
-100
0
100
200
300
400
500
600
700
800
900
1000
Model
NO2.10
Rad. Sol.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: DEVELOPMENT
NEURAL NETWORK RESPONSE GRAPH: correlation between
NO2 and Vertical Gradient of Temperature
Response Graph, NO2 (10 )
62
61
60
59
58
57
56
NO2
55
54
53
52
51
50
49
48
47
46
45
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
Model
NO2.10
GTV
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: Observed versus predicted
Neural Net data during the hours of the average week of the month
for the first quarter of the year
Observed
Predicted
120,0000
100,0000
80,0000
60,0000
40,0000
20,0000
0,0000
1
18 35 52 69 86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 375 392 409 426 443 460 477 494
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: Observed versus predicted
Neural Net data during the hours of the average week of the month
for the second quarter of the year
Observed
Predicted
100,0000
90,0000
80,0000
70,0000
60,0000
50,0000
40,0000
30,0000
20,0000
10,0000
0,0000
1 18 35 52 69 86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 375 392 409 426 443 460 477 494
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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MODELS: Observed versus predicted
Neural Net data during the hours of the average week of the month
for the third quarter of the year
Observed
Predicted
120,0000
100,0000
80,0000
60,0000
40,0000
20,0000
0,0000
1 18 35 52 69 86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 375 392 409 426 443 460 477 494
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
22
MODELS: Observed versus predicted
Neural Net data during the hours of the average week of the month
for the last quarter of the year
Observed
Predicted
90,0000
80,0000
70,0000
60,0000
50,0000
40,0000
30,0000
20,0000
10,0000
0,0000
1 18 35 52 69 86 103120137154171188205222239256273290307324341358375392409426443460477494511
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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CONCLUSIONS
&
FUTURE STEPS
 The aim of this work has been the calibration of a neural network, in order to
forecast the roadside pollutant levels, using the data measured by pollutants detection
unit of the Risorse Ambiente Palermo site in Castelnuovo square into the centre of
Palermo, in order to evaluate traffic management measures able to reduce the
monitored traffic pollutants such as NOx and CO.
 We have focused this work on NO2 forecasting because there is a systematic
overcoming in Palermo in the last years of the threshold imposed by the law in force
concerning the annual average of NO2.
 The CO level has been used as proxy variable of the traffic conditions, taking into
account the analysis carried out comparing traffic flow and CO level during three
consecutive months in a urban road of Palermo.
 The estimated Neural Net has understood the correlation between NO2 and the
input variables, as it has been shown plotting the response graph between NO2 and each
input variable (NO2 1 hour before, CO, wind speed, solar radiation, Vertical Gradient of
Temperature).
 It has been plotted the comparison between observed and predicted by Neural
Net data during the hours of the average week of the month for a year, showing a high
level of correlation (0,91).
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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CONCLUSIONS
&
FUTURE STEPS
 As further work, it has been planned to
extend the experimental analysis in other critical
points of the network, especially in the canyon
roads with traffic lights, using a mobile laboratory
equipped for measuring pollutant data.
 Another relevant point of the research will be
the evaluation of traffic management measures
applied to urban transport network of Palermo, in
order to reduce the pollutant levels.
“ Climate Change Targets and Urban Transport Policy” 13 – 14 April 2015, University of Malta Valletta Campus
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THANK YOU!
marco.migliore@unipa.it
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