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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 2233 road stretches belonging to all different types of urban roads: • principal arterial roads • collector arterial roads • local 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 Road category 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 Fermi_AD 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