ENVIRONMENTAL ASSESSMENT IN URBAN SETTINGS: ROAD TRAFFIC NOISE IN THE METROPOLITAN AREA OF THE STRAITS OF MESSINA

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1 Twelfth International Congress on Sound and Vibration ENVIRONMENTAL ASSESSMENT IN URBAN SETTINGS: ROAD TRAFFIC NOISE IN THE METROPOLITAN AREA OF THE STRAITS OF MESSINA Francis Cirianni - Giovanni Leonardi DIMET, Faculty of Engineering, Mediterranea University of Reggio Calabria Feo di Vito Reggio Calabria, Italy leonardi@ing.unirc.it Abstract The aim of the study presented is the definition of a methodological approach for the quantitative analysis of the level of noise produced on and by transport infrastructures in urban settings. Noise pollution is among the environmental impacts produced by road traffic which represent a health hazard in urban settings. It is thus necessary to dispose of adequate and specific mathematical tools which can reproduce e/or simulate different acoustic scenarios. The analysis of the experimental data measured in metropolitan area of the straits of Messina, which for its particular location is always subject to heavy vehicular traffic to and from Sicily, are presented, and the output used to calibrate a forecasting model: the results are also confronted with the outputs of forecasting models known and used in literature. INTRODUCTION The adoption of particular models of calculation for the forecasting of noise pollution by road vehicles is fundamental for the design of new infrastructures or in case of transport planning and policy decision making [1] [2]. Such models are based essentially on the use of opportune formulas of regression that estimate the existing correlations between the levels of noise and some not acoustic parameters relative to the source and the distance of propagation. In the case presented the sound levels are calculated according to some specific parameters that characterize the vehicular traffic and the geometric and morphologic 1

2 characteristics of the survey site [3]. The Integrated Area of the Straits of Messina has been chosen for its territorial characteristics for the study and application of an appropriate methodology of analysis for the forecasting and assessment of the noise pollution caused by motor vehicles that daily cross this area. The area, which owes its importance to its geographic location being the gateway to Sicily, formed by the cities of Reggio Calabria on the continental side and Messina on the Sicilian side, is an area which just falls short of a million inhabitants. Heavy traffic crossing the Straits, a relatively high population density (1000 inhabitants per square km) and the growth of car ownership in absence of a transport policy has resulted with congested local traffic. The area, which is at the centre of the plan for the Bridge over the Straits of Messina, a bridge of 3.6 km in length which will have a great impact on the urban environment in general and the transport network in particular, with significant impacts, not only when operating, but also during the construction phase. The development of flexible and dynamic tools of analysis of noise pollution will also be useful to provide support to the strategic planning to diminish the environmental impacts on the territory. The area object of study is strongly conditioned from the system of transport connections between Calabria and Sicily, which is essentially concentrated in the two terminals of Villa S. Giovanni and Messina. All traffic flows cross these two nodes that in the years have grown in terms of shipping terminals, for road and rail traffic as to satisfy in a more or less efficient way all the demand. However, the presence of heavy through traffic crossing the nodes of Messina and Villa S. Giovanni, has not seen a proportional evolution on the characteristics of the systems and little transformations have happened, and the actions have been concentrated mainly on the terminals, and in part on the access routes, but without an organic and planned scheme for the crossing system as a whole. In fact, the two cities have endured the presence of through traffic without it reflecting in the organization of the structure and in the city design, and with little, if none, advantages in the growth of the urban expansion. But undoubtedly the congestion caused on the city roads, crossed by the through traffic linking an island where its resident population is almost one tenth on the whole of the Italian population, causes devastating impacts in terms of noise and air pollution. On the Sicilian side, the vehicles in transit to and from the continent must obligatorily cross the city of Messina, and in particular there are two alternatives: 1. landing at the National Railway (RFI) terminal at the S. Raineri zone, and crossing the city along via La Farina up to the motorway. 2. landing at the private ferry companies terminal and crossing the city along viale della Libertà and viale Boccetta up to the motorway. On the coast of Calabria, instead, the connections between the shipping terminal and the Motorway presents critical points of low capacity and elevated interferences with the urban settings; so the presence of queues which extend back all the 2

3 connection are not unusual. The National Railway terminal is connected directly to the motorway parking area, therefore the road traffic is connected directly to the motorway by means of a two lane underpass not interfering with the city of Villa S. Giovanni. The traffic ferrying with the private companies must, instead, cross the city roads, along via Italia and a long stretch of promenade along the sea front. CAMPAIGN OF FIELD MEASURES AND EXPERIMENTAL SURVEY A survey on the quality of the acoustic climate has been led, on the basis of the exposed considerations, on the evidence of the peculiar aspects and the criticalities due to the trough traffic flows in the area of the Straits crossing from and to the island of Sicily, concentrated on the two territorial areas of the cities of Villa S. Giovanni, on the continental side and of Messina on the Sicilian side. The measures campaign, therefore, has been carried out in the two cities in the week days in the period of September-November 2003, defining altogether 14 survey sites (5 in the city of Villa S. Giovanni and 9 in the city of Messina). Site 1 VSG - Via Garibaldi, Largo stazione Site 8 ME - Via Taormina Site 2 VSG - Via Italia, Motorway Exit Site 9 ME - Via Garibaldi Site 3 VSG - Piazzale Anas Site 10 ME - Viale della Libertà Site 4 VSG - Via Italia Site 11 ME - Viale Boccetta Site 5 VSG - Strada Statale 18 Site 12 ME - Viale Europa Site 6 ME - Piazza Repubblica - FS Station Site 13 ME - Via Taormina bis Site 7 ME - Via La Farina Site 14 ME - Marittima Station VSG: Villa San Giovanni ME: Messina Once that the survey sites have been chosen, in order to obtain a punctual assessment of the environmental noise, the chosen parameters are the equivalent level L eq, and the following percentile levels: L 10, L 50 and L 90 (they represent the levels of noise measured respectively for 10%, 50%, 90% of the time). NOISE FORECASTING MODELS The models proposed in literature, differentiated by various levels of complexity, demand the knowledge of a series of parameters to be estimated experimentally, to define some coefficients to adapt to the specific studied situations [3]. Such coefficients are influenced unavoidably by the measure conditions in which they have been defined. Therefore it follows a not immediate transportability of the model to different kind of street and of traffic configurations from those for which the models have been calibrated. The models more frequently used are characterized from a structure which can be expressed analytically by means of the following synthetic relation: 3

4 0 n i ( i) (1) L= a + a f x i= 1 In which L represents the selected indicator in order to describe the noise event (usually the weighted equivalent level L Aeq, or the percentile levels L x ) and f(x i ) indicates a function, generally logarithmic, of different not acoustic variables x used for the determination of the sound levels. Surveys carried out in the cities of Villa S. Giovanni and Messina It was chosen to carry out the measures during day hours, in an interval of time between 8:00 a.m. and 7:00 p.m. and altogether the data is divided in 9 hour slots. The sampling interval inside the hourly slots has been of 15 minutes. For the 14 monitoring sites, the following characteristics were recorded: 1) geometry (width of the track, width of the sidewalks on the monitoring side and the opposite side, number of available lanes for the vehicular traffic, height of the buildings on the monitoring side and on the opposite side); 2) the composition of the traffic flow; 3) the average running speed; 4) the levels of noise; 5) marching directions; 6) parkings; 7) characteristics of street paving; 8) longitudinal slope. For the kind and composition of the traffic flow the mobile sources of noise have been classified, subdivided in the following categories: a) motorcycles and cycles; b) motor vehicles; c) light goods vehicles; d) heavy goods vehicles; e) buses. Once the data base containing all the useful information for a complete and exhaustive acoustic-morphologic characterization of the studied sites has been set up, the independent variables of input for the definition of the regressive forecasting models can be defined. It must be remembered that starting from the traffic data and other not acoustic variables, by the application of the models it is possible to find the equivalent levels of corresponding noise. Therefore, the coefficients of correlation were estimated with the aim to characterize the variables that mainly influence the levels of noise. The statistic analysis led preliminarily has allowed to characterize the two regressive models that better seem to adapt themselves to the peculiarities of the study area [4]: the first one is an evolution of the model CRTN (Calculation of Road Traffic Noise), developed from the Department of Transport (UK), for the assessment of the statistical level L 10 db(a) of the noise from street traffic; the equation proposed by the CRTN model is the following: 500 5p L10 = 10 log Q+ 33log V log G 27.6 V V (2) where the considered variables are: 1) the flow Q(veic/h); 2) average speed V(km/h); 3) the composition of the vehicular traffic (in terms of percentage of 4

5 heavy vehicles with empty weight greater than 1525kg) p(%); 4) the slope of the road G(%); from (2) the following general equation for the calculation of L eq is obtained: p L = α log Q+ α log V α log 1+ + α G+ α V V R1: eq (3) the second, instead, allows to estimate the acoustic levels, introducing the concept of equivalent flow Q e, by the relation: 15 L = β log Q + β log + β V + β G+ β d R2: eq 1 e (4) Q e (veic/h) holds account of the presence of heavy vehicles (assuming 1HV = 6LV and 1 motorcycle = 2LV), while d (m) measure the width of the semi track. Through a successive not linear regressive analysis the coefficients α 1, α 2, α 3, α 4, α 5 and β 1, β 2, β 3, β 4, β 5 have been estimated, obtaining: p L = 5.98 log Q log V log G eq V V 15 Leq = 4.42 log Qe 0.03 log V G d (5) (6) Leq measured R1 (R=0.74) R2 (R=0.73) Figure 1 Measured and estimated L eq DEFINITION OF A FORECASTING FUZZY MODEL In parallel with the modelling with the given models, the application of neural-fuzzy models to the forecasting problem of acoustic pollution is presented, applying adaptive a neural-fuzzy inference system (ANFIS). These techniques provide a method for the fuzzy modelling procedure to learn information about a data set, in order to compute the membership function parameters 5

6 that best allow the associated fuzzy inference system to track the given input/output data. This learning method works similarly to that of neural networks. The ANFIS using a given input/output data set constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either back propagation algorithm alone, or in combination with a least square type of method. This allows the fuzzy system to learn from the modelling data [5]. Fuzzy models Fuzzy modelling is usually based on rule-based models. The most common alternatives are linguistic fuzzy, which suit well extracting expert knowledge. The employed fuzzy model in the present study refers to a particular method of inference called Takagi-Sugeno-Kang (TSK) which allows to construct the system of analysis from a set of example data [6]. Takagi-Sugeno-Kang models are suitable for approximation of a large class of non-linear systems. In Takagi-Sugeno-Kang (TSK) fuzzy model, the rule consequents are crisp functions of the model inputs [7]: R i : If x 1 is A i1 and x 2 is A i2 and.. and x n is A in then y i = a i x + b i with i =1, 2,, K, where R i is the i-th rule, x 1, x 2,..., x n are the input variables, A i1, A i2,.., A in are the fuzzy sets assigned to corresponding input variables, variable y i represents the value of the i-th rule output, and a i and b i are parameters of the consequent function. The final output of the TSK fuzzy model ( y ˆk ) for an arbitrary x k input sample is the weighted average of all rule outputs, calculated using the following expression: ( x ) y ( x ) β ( x ) k β i= 1 j k i k ˆ k =, = 1, 2,3,..., k i= 1 i k y k n where y i (x k ) is output of i-th rule for x k (k-th) input sample, and β i represent the firing strength of the i-th rule. The aim is to generate a TSK fuzzy model through a learning process on the base of a data set of input/output. The parameters associated with the membership functions will change through the learning process. The computation of these parameters (or their adjustment) is facilitated by a gradient vector, which provides a measure of how well the fuzzy inference system is modelling the input/output data for a given set of parameters. Once the gradient vector is obtained, any of several optimization routines could be applied in order to adjust the parameters so as to reduce some error measure (usually defined by the sum of the squared difference between actual and desired outputs). At the end more computational simplicity was obtained using clustering methods for partitioning the input output. The purpose of clustering is to find natural groupings of data in a large data set, 6

7 thus revealing patterns in the data that can provide a concise representation of the data behaviour. The learning data, divided into these information clusters, are then interpreted as rules. Methods of fuzzy clustering, such as fuzzy C-means 1 (FCM), are convenient tools for the process of partitioning of the input space or input output space. Clustering algorithms typically require the user to pre-specify the number of cluster centres and their initial locations; the locations of the cluster centres are then adapted in a way such that the cluster centres can better represent a set of archetypical data points covering the range of data behaviour. If user doesn t have a clear idea how many clusters there should be for a given set of data, subtractive clustering can be applied this is a fast, one-pass algorithm for estimating the number of clusters and the cluster centres in a set of data. In the subtractive clustering method each data point is considered as a potential cluster centre. The cluster estimates obtained using the subtractive clustering method can be used to initialize iterative optimization-based clustering methods and model identification methods [7]. INPUT INPUTmf RULE OUTPUTmf OUTPUT Model Construction Figure 2 ANFIS architecture The modelling approach used is similar to many system identification techniques. Initially a parameterised model structure is assumed, then the training phase is started for the validation of the model. Model validation is the process by which the input vectors from input/output data sets on which the fuzzy inference system (FIS) was not trained, are presented to the trained FIS model, to see how well the FIS model predicts the corresponding data set output values. To start training an FIS first it is essential to build a training data set that contains desired input/output data pairs of the target system to be modelled. 1 Fuzzy C-means (FCM) is a data clustering technique where each data point belongs to a cluster to some degree that is specified by a membership grade. It provides a method that shows how to group data points that populate some multidimensional space into a specific number of different clusters. 7

8 Sometimes it is also necessary to have a checking data set that is used to control the potential for the model over fitting during the training. Therefore, after having defined as input variables the total flow (Q tot ), the percentage of heavy vehicles (p), average speed (V) the lane width (d) and as output the equivalent sound level (L eq ), the available data is divided, following mainly random criteria, in two sets: the first for the training phase (training data) and the second for the checking phase (checking data), in particular, 94 data points were used as training data and 60 data points as checking data. To optimize the response of the designed model the trend of RMS (root mean square error) was analysed as the training phase varied (figure 3) RMS training checking Epoch Figure 3 Training and checking error After the training (60 epochs) the model has improved a lot with respect to the training data, but only a little with respect to the checking data, furthermore it has been noticed that past the 45 th epochs the system no longer does a very good job of fitting the checking data. The result is a loss of generality so the training process was interrupted at the 45 th epoch when the smallest value of the checking error occurs. Completed the phase of training, it can be observed as the ANFIS model succeeds to describe the problem adequately, in fact, the forecast error of the answers is contained within acceptable values, and furthermore the model guarantees always a greater precision in respect to all the considered regressive models (figure 4). 82 training data 78 checking data Leq measured Leq estimated Leq 74 Leq Leq measured Leq estimated Figure 4 Model performance for training and checking data 8

9 Finally in the following table the characteristic values of the performance for the R1, R2 and ANFIS models are shown. RMS Mean error Correlation Standard deviation ANFIS R R training checking Table 1 Characteristics of the models CONCLUSIONS Main aim of the presented study is the analysis of the acoustic climate of the area between Sicily and Calabria interested by the crossing and the future construction of the bridge on the Strait of Messina. Therefore, following a significant campaign of measures, a methodological approach for the forecasting of noise pollution due to vehicular traffic was adopted, defining two regressive models opportunely calibrated with the data measured in relation to the transit of vehicles crossing from and to Sicily. Furthermore, a fuzzy model of Takagi-Sugeno-Kang (TSK) constructed with method ANFIS on the base of the measured data set was applied. Both the proposed approaches have demonstrated a good reliability allowing to face the study of the urban noise produced by vehicular traffic, using as known variables only the characteristics of traffic and of roads (table 1). REFERENCES [1] Corriere F., Lo Bosco D., Valutazione previsionale dell inquinamento acustico nella viabilità urbana. Autostrade, N. 1, [2] Cirianni F., Leonardi G., The application of a neural network on a study of noise pollution in urban transport: a case in Villa S. Giovanni. Air Pollution XII, WITpress, [3] ANPA Agenzia Nazionale per la Protezione dell Ambiente, Rassegna dei modelli per il rumore, i campi elettromagnetici e la radioattività ambientale. RTI CTN_AGF 1/2001. [4] Steele C., A critical review of some traffic noise prediction models. Applied Acoustics, Elsevier, [5] Isokangas A., Juuso E., Fuzzy modelling with linguistic equations [6] Kukolj D., Design of adaptive Takagi Sugeno Kang fuzzy models. Applied Soft Computing, Elsevier, [7] Chiu S., Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification. Fuzzy Information Engineering: A Guided Tour of Applications,