Traffic Model Uncertainty For Noise Mapping

Universit` a di Pisa Facolt` a di Scienze Matematiche Fisiche e Naturali

Corso di Laurea Specialistica in Fisica Applicata Anno Accademico 2007-2008

Tesi di Laurea Specialistica

Traffic model uncertainty for noise mapping Incertezza associata all’uso di modelli di traffico per la redazione di mappe acustiche

Candidato ELENA ASCARI

Relatore Chiarissimo Prof. G.LICITRA

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Abstract After the European Parliament published the Environmental Noise Directive 2002/49/CE (END) and the implementation by Member States in their own legislation, they had to use the same evaluation methods to analyse noise pollution and the same indicators suggested by the END (LDEN end LN ight ). The aim of the END is an international comparison between European countries, through strategic noise mapping and action planning. Moreover noise mapping is nowadays the principal way for Italian public administration to manage noise pollution and to draw up acoustic mitigation plans. So municipalities ought to have reliable maps and dynamic maps easily modifiable, to mirror changes over traffic flow circulation. Following this approach, this work is focused on setting up Pisa road noise map based on a traffic model. Because of lots of inputs data requested, it’s necessary to evaluate accuracy of final product based upon goodness of inputs. This evaluation has been carried out by the European commission and officially published in 2007 as the Good Practice Guide. This paper will apply this guide to verify reliability of suggested accuracy in noise mapping and to evaluate uncertainty associated with predicted noise levels, when measurements are used to validate mathematical models.

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Thanks I want to sincerely thank all members of U.O. IMREC (Mobility Infrastructures, Electric and Communication Networks) of Pisa ARPAT department for help, patience and willingness during my stay: special thanks to architect C.Chiari for her GIS knowledge, to Grad. M.Reggiani to teach me software IMMI, to Grad. M.Cerchiai, D.Simonetti and A.Panicucci to make possible the presentation of strategic map. Moreover very special thanks to Fabrizio Balsini to teach me and help me using instruments to perform both sound levels and flow measurements. I want also to thank PhD. G.Memoli always placed at my disposal for explanations. Finally thank to Prof. G.Licitra and Prof. P.Gallo which support me and this work.

Contents 1 Introduction

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2 Objectives

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3 The 3.1 3.2 3.3 3.4 3.5

NMPB method for road traffic noise 5 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Guide du Bruit: sound emission DB for light and heavy vehicles 5 NMPB-Routes-96: meteorological correction . . . . . . . . . . 9 Source characterization . . . . . . . . . . . . . . . . . . . . . 11 Attenuations due to propagation . . . . . . . . . . . . . . . . 12

4 Traffic and noise models implementation 4.1 IMAGINE project . . . . . . . . . . . . . . . . . . . . . 4.1.1 Use of traffic models to evaluate road noise levels 4.2 Good Practice Guide . . . . . . . . . . . . . . . . . . . . 4.2.1 Accuracy evaluation: toolkits . . . . . . . . . . . 4.3 European noise mapping experiences . . . . . . . . . . . 4.3.1 Noise mapping of Pamplona agglomeration . . . 4.3.2 Noise mapping of Scottish agglomerations . . . . 4.3.3 Noise mapping pilot project in Portugal . . . . . 4.4 Tuscany case studies . . . . . . . . . . . . . . . . . . . . 4.4.1 Florence road noise map . . . . . . . . . . . . . . 4.4.2 First noise map of Pisa . . . . . . . . . . . . . . 4.4.3 Two wheelers sound emission evaluation . . . . .

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5 A new approach to traffic assessment 35 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 TransCAD characteristics . . . . . . . . . . . . . . . . . . . . 36 5.3 User Equilibrium Method . . . . . . . . . . . . . . . . . . . . 37 5.4 Frank Wolfe algorithm . . . . . . . . . . . . . . . . . . . . . . 40 5.5 Transport network . . . . . . . . . . . . . . . . . . . . . . . . 41 5.5.1 Data collected from first step of TransCAD utilization 43 5.5.2 Road classification and input data . . . . . . . . . . . 43 5.5.3 Intersection delays . . . . . . . . . . . . . . . . . . . . 45 iv

CONTENTS 5.6

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OD matrix calculation . . . . . . . . 5.6.1 Sample counts . . . . . . . . 5.6.2 Equivalent vehicles . . . . . . Flow and speed network assignment Model validation . . . . . . . . . . . Uncertainty evaluation . . . . . . . .

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6 TransCAD traffic output elaborations 55 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.2 Spatial resolution . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.3 Temporal resolution . . . . . . . . . . . . . . . . . . . . . . . 57 6.4 Traffic data resolution . . . . . . . . . . . . . . . . . . . . . . 58 6.4.1 From equivalent vehicles to real vehicles . . . . . . . . 58 6.4.2 From real vehicles to NMPB light and heavy vehicles . 59 6.5 Accuracy of correction coefficients . . . . . . . . . . . . . . . 60 7 Noise model implementation 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 3D model implementation . . . . . . . . . . . . . . . . . . 7.2.1 Digital Terrain Model DTM . . . . . . . . . . . . . 7.2.2 Bridges and viaducts . . . . . . . . . . . . . . . . . 7.2.3 Sound barriers and walls . . . . . . . . . . . . . . . 7.2.4 Buildings and streets: GoogleEarth utilization . . 7.3 Calculation settings . . . . . . . . . . . . . . . . . . . . . 7.3.1 Source settings and meteorological conditions . . . 7.3.2 Propagation: reflections and absorption coefficients 7.3.3 Automated distributed calculation: segmentation . 7.3.4 Grid resolution . . . . . . . . . . . . . . . . . . . . 7.4 Fa¸cade calculation and population exposure . . . . . . . . 7.5 Accuracy evaluation . . . . . . . . . . . . . . . . . . . . . 8 Noise mapping results 8.1 Noise road map . . . . . . . . . . . . . . . . . . 8.2 Population exposure to road noise . . . . . . . 8.3 Accuracy results . . . . . . . . . . . . . . . . . 8.3.1 Theoretical accuracy: global uncertainty 8.3.2 Available measurements reliability . . . 8.3.3 Residuals distributions . . . . . . . . . . 8.4 Comparison with previous map results . . . . . 8.5 Strategic noise map . . . . . . . . . . . . . . . . 8.5.1 People exposure to global levels . . . . . 8.5.2 Conflicts maps . . . . . . . . . . . . . . 9 Conclusions and developments

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CONTENTS

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A Acoustics basics

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B Road noise maps

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C Strategic noise maps

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Chapter 1

Introduction After the European Parliament published the Environmental Noise Directive 2002/49/EC (hereinafter END, [1]) and the implementation by Member States in their own legislation, they had to use the same evaluation methods to analyse noise pollution and the same indicators suggested by the END (LDEN and LN ight ). The aim of the END is an international comparison between European countries especially using strategic noise maps1 and action plans2 . Member States have to ensure that maps and action plans will be write up in these different situations: agglomerations with more than 250.000 inhabitants, major roads with more than 6 millions vehicles per year, major railways with more than 60.000 trains per year and major airports with more than 50.000 movements per year (maps no later than 30/6/07, plans no later than 18/7/08); agglomerations with more than 100.000 inhabitants, major roads with more than 3 millions vehicles per year, major railways with more than 30.000 trains per year (maps no later than 30/6/12, plans no later than 18/7/13). These dead lines have been adopted in Italy by the D.L. n.194 19/8/05 which transposes the END, establishing endorsements for defaulting authorities [2]. However, noise mapping is nowadays the principal way for public administrations to manage noise pollution even in context different from the ones imposed by the END: in fact, an acoustic map is not only a photo of the acoustic conditions of a single infrastructure, but also a dynamic instrument for urban planning. Especially in Italy, strategic noise mapping is the first step of Italian action plans (hereinafter PCRA) which must be drawn up by municipalities in order to attempt DM Ambiente 29/11/00, [3]. Authorities have to verify if noise levels exceed limits established by local acoustic classification plans (hereinafter PCCA) defined in DPCM 14/11/97, [4]. Therefore, using re1

A map designed for the global assessment of noise exposure in a given area due to all sources. 2 Plans designed no manage noise issues and effects, including noise reduction.

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CHAPTER 1. INTRODUCTION

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sults of an acoustic map, authorities may set up strategic measures which could modify inhabitants life in terms of streets furniture (e.g. barriers, absorbent asphalts. . . ) or circulation changes (e.g. limited traffic zones, lowering of speed limits, traffic circles. . . ). On the other hand, municipalities ought to have reliable maps and at the same time dynamic maps easily modifiable to mirror changes in traffic flow circulation. Following this approach, this thesis is focused on setting up Pisa noise map based on a traffic model. However, it’s always been critical to map very large zones as an entire municipality, not only because calculation models may take long run time, but also because we need to collect lots of input data not always available. We have to do many assumptions and to estimate as many parameters to carry out acoustic maps (and more have to be done using traffic model): therefore, we evaluated goodness of final product based on detail and accuracy of inputs, according evaluation carried out by the European commission and officially published in 2007 as the Good Practice Guide (hereinafter GPG)[5]. This document is fundamental for this work because it provides accuracy for each modelling choice. This thesis will present the noise map for road source in the municipality of Pisa using a traffic model and it will evaluate the uncertainty associated with estimated noise levels: this work is continuous with previous acoustic map of Pisa written up in 2006 [6]. Although it wants to improve the level of deepening and reliability making a different assignment of traffic flows; this approach should be an improvement of the assignment based on road classification (fixed flow for each class) used for the first map and it’s forecasted by GPG [5] and suggested also in [6]. Both software TransCAD (traffic flows calculation) and IMMI (noise level calculation) will be presented with special attention to methods implemented and to all elaborations needed to use the output of the first as input of the second. Moreover, this work is part of the broader project of strategic noise map: so results of strategic map will be presented taking into account also railway and airport noise. Strategic noise map is a project of regional environmental agency ARPAT carried on in Pisa department by U.O. IMREC (Mobility Infrastructures, Electric and Communication Networks Unit): therefore this work has been done in ARPAT department.

Chapter 2

Objectives The aim of this study is to write up the road noise map of Pisa municipality using a traffic model for vehicles flows calculation and its validation through measurements to verify reliability of accuracy suggested by GPG. Therefore, we want to evaluate uncertainty associated with residuals between measurements and calculated levels to show accuracy of used modelling methods. First of all, we have to estimate vehicles flows on the whole municipality, that is: • create town road network including principal roads; • measure flows on sample roads to calibrate model; • assign traffic with TransCAD; • measure flows on sample roads to validate model; • insert roads not included in traffic assignment; • evaluate traffic flows on these roads (based on previous map taking into account recent modifications). After having number of vehicles per road, we need to estimate sound power levels: unfortunately, noise emission model imposed by the END establishes sound power level for two vehicles categories which don’t correspond to calculated vehicles1 . Therefore, we need to elaborate data before being inserted in sound emission model: disaggregation of results in categories requested by French sound emission model NMPB (official method, see D.L. 19/8/05) is performed together with sound levels measurements related to traffic flows ones to verify the reliability of disaggregation itself. The next step has been the creation of an IMMI project including necessary cartography to set up a digital terrain model. Moreover, 3D model has been 1

Causes of this mismatch are fully explained in following chapters.

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CHAPTER 2. OBJECTIVES

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prepared through 3D views and eye inspections: we used georeferred photos from GoogleEarth imported by the software and 3D visualization on Live Search Map website. Then sound levels calculation has been done with a 5 m step grid through which we assign sound fa¸cade levels to residential buildings with Italian and European indicators. After that, results have been evaluated performing • comparison between available measurements and calculated levels to estimate accuracy; • evaluation of theoretical accuracy from GPG and other considerations not included in that paper; • comparison between theoretical and real accuracy. Moreover, thanks to population data of 2001 census, it has been possible to calculate people exposure to road noise levels and the global exposure from the analysis of strategic noise map.

Before going through each step of this project, we want to explore the state of art of noise mapping and international documents that must be taken into account approaching noise mapping according the END: • The Guide du Bruit and the French method NMPB indicated in the END as the official method for road traffic noise; • The IMAGINE project (Improved Methods for the Assessment of the Generic Impact of Noise in the Environment, see www.imagineproject.org): this project analysed different techniques of noise mapping, included the ones with traffic model, emphasizing general difficulties; • The Good Practice Guide ; • European mapping experiences; • Road noise map in the city of Florence published in February 2008 by ARPAT and correlated studies on sound emission levels of different categories of vehicles; • Previous noise map of Pisa; • ARPAT study on sound emission of two wheelers vehicles carried out during evaluation of acoustic clime in Pisa.

Chapter 3

The NMPB method for road traffic noise 3.1

Introduction

The calculation model for road traffic noise definitively adopted by the END is the French official method NMPB-Routes-96 (SETRA-CERTU-LCPCCSTB), cited in Arrˆet´e du 5 mai 1995 relatif au bruit des infrastructures routi`eres, Journal Officiel du 10 mai 1995, article 6 and in French law XPS 31-133 and successively adjusted to European indicators in commission recommendation 2003/613/EC. The NMPB (Nouvelle M´ethode de Pr´evision du Bruit, [7]) takes into account meteorological effects on long distance propagation: so it’s useful for modelling big infrastructures (as the ones to be mapped according the END) in free field propagation conditions (its limit of application is 800 m far from the source). However, the critical problem of this model is the sound emission database: in fact, considered vehicles categories are not always suitable to all European countries (two wheelers are not considered) and it’s not at all updated because the database is the same of the Guide du Bruit of 1980 [8]. In next section the sound emission database is presented.

3.2

Guide du Bruit: sound emission DB for light and heavy vehicles

Vehicles are treated as a point isotropic source 80 cm height above road line: point approximation is good for almost all situations but we have to pay attention when barriers are installed near the source (in this case ad hoc calculation method has been set up); the isotropic approximation is broken only by very big heavy vehicles. With this kind of model, the vehicle sound power W is correlated to the

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CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 6 sound pressure p through next expression: W =

p2rms 2πr2 ρ0 c

in which ρ0 is air density, c is sound speed and r is the distance from the source. The sound power has to be compared to the reference power source (10−12 Watt) emitted on a sphere so the sound power level is given by: LW = Lp + 20 log r + 8 The level above is referred to a single vehicle emitting in a semi-sphere but we can calculate the sound power level for units length considering the number of vehicles per hour Q and the averaged speed v expressed in km/h:   Q (LW )m = LW + 10 log 1000v in which LW is the sound power level associated to a point source whose equivalent length will be fully described in section 3.4. Database values are emission levels, that is the sound pressure level of a single passage in an hour measured 30 m far from the source and at 10 m above road surface. Sound pressure must be integrated in the referred time interval (i.e. an hour): Z  2 t2 1 t2 W ρo c p t1 = dt T t1 2πr2 This relationship can be rewritten considering a point source moving at speed v and at a distance d under an angle of view θ:  2 t2 1 W ρo c θ p t1 = T 2π d · v So we can write: [Leq ]tt21 = LW − 10 log(d · v) − 8 − 10 log T + 10 log θ Now, using an hour as time interval and π as angle, we obtain that the equivalent hourly level of a single passage is given by: Lh,eq = LW − 10 log(d · v) − 38 and for Q vehicles: Lh,eq = LW − 10 log(d · v) − 38 + 10 log Q Therefore, if we take into account the reference distance (and light air absorption) and speed in km/h, the sound emission level is defined by this equation: E = LW − 10 log v − 50 = (LW )m − 20

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 7 These emission values are given by the Guide du Bruite for two vehicles class, for different type of circulation and type of slope of the road. Vehicles classes are: • light vehicles: under 3.5 ton full-load; • heavy vehicles: over 3.5 ton full-load. Diversification of circulation type is based on average acceleration: • fluid and continuous: vehicles number is constant on time and space, there aren’t accelerations; • pulsed continuous: vehicles number and speed vary along time although it’s possible to define an average speed; • pulsed accelerating: majority of vehicles is accelerating; • pulsed decelerating: majority of vehicles is decelerating. Types of slopes are: • horizontal: ramp with inclination under 2%; • ascending: ramp with inclination over 2% in ascending direction; • descending: ramp with inclination over 2% in descending direction. In figure 3.1 the noise emission database from [8] is represented and in the following table we show values for some typical speeds and for all types of circulation in an urban context (emission values are given for an horizontal road). Table 3.1: Circulation Type Fluid Continuous

Pulsed continuous Pulsed accelerating Pulsed decelerating

Emission Levels in urban context speed E light vehicles E heavy vehicles [km/h] [dBA] [dBA] 30-50 29.5 44.0 50-70 30.5 42.5 60-80 32.0 43.0 30-60 31.5 43.5 low 37.0 47.0 high 33.0 43.0 29.0 36.0 - 38.0

Finally, we define emission of Ql light vehicles (EQl ) and of Qp heavy ones (EQp ):  EQl = El + 10 log Ql EQp = Ep + 10 log Qp

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 8

Figure 3.1: Noise emission database [8]

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 9

3.3

NMPB-Routes-96: meteorological correction

In previous sections we explained that innovation of this method is how meteorological effects are treated. Acoustically speaking, meteorological conditions are divided in three classes: 1. homogeneous conditions: sound energy propagates along straight lines; 2. conditions favourable to sound propagation: sound energy get down toward ground giving much noise at receivers; 3. conditions unfavourable to sound propagation: sound energy rise toward the sky giving less noise at receivers. NMPB considers only first and second conditions, not only because the third is difficult to calculate, but also because this assumption overestimates levels and so it’s safer. The origin of meteorological effects on propagation is due to combination of thermal gradient with aerodynamic factors of wind directions; in homogeneous conditions these factors balance their selves, instead in favourable ones acoustic rays go downwards. In fact, favourable condition occurs when wind direction is the same as propagation one and when thermal gradient is positive (hot air is up) that means sound speed increases with distance from ground (c ∼ = 331.6 + 0.6Tc , in which Tc is temperature in Celsius degrees). We obtain the effect shown in figure 3.2.

Figure 3.2: Favourable Conditions [9] The model calculates sound level separately for each meteorological condition, obtaining LF for favourable conditions and LH for homogeneous ones; after that, long term level is estimated by: h i LF LH LLT = 10 log p10 10 + (1 − p)10 10 in which p is favourable conditions probability. The NMPB [7] provides this probability for some French cities and suggests

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 10 to use 50% for day period and 100% for night period for all places where meteorological databases are not available; that values of probability descends from the observation of night temperature inversion. Calculation process implemented by NMPB is described in figure 3.3.

Figure 3.3: General flow chart of NMPB method

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 11

3.4

Source characterization

Road source is represented by many point sources located along the centre line. Sectioning a road infrastructure in point sources needs to identify acoustic homogeneous arcs: that means to define arcs as road sections with the same geometrical profiles and with constant traffic flows. Each arc is then split up into point sources to assign sound power. This splitting up may be done in different ways: • Equiangular splitting up1 : the site is scanned from the considered receiver point by a group of rays whose angle step is constant (the more a receiver is close to source the more the step is small) and at each intersection of one of these rays with a source line, a point source is placed; • Splitting up with a constant step: each source line is split up into point sources regularly spaced out (the step between two consecutive sources does not have to be greater than half the orthogonal distance between the lane and the closest receiver point and the value of the step shouldn’t be greater than 20 m) • Variable splitting up: as the first method but with local variation of angular step; Finally, the sound power level for octave band j of each point source is given by:   LAW i = 10 log 10

EQ l 10

+ 10

EQ p 10

+ 20 + 10 log li + R(j)

in which li is length (in meters) of road section represented by the current point source i and expressed in figure 3.4;

Figure 3.4: Length of road section represented by point source i and in which R(j) is road noise normalized A-weighted spectrum given in the next table: 1

This method is the once implemented by IMMI.

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 12

Table 3.2: Road noise Spectrum j frequency [Hz] R(j) [dBA] 1 125 -14 2 250 -10 3 500 -7 4 1000 -4 5 2000 -7 6 4000 -12

3.5

Attenuations due to propagation

Sound power level of a point source has to be expressed as sound pressure level: to do this we have to take into account all attenuations of propagation. So, we have to calculate a different level for homogeneous and favourable conditions:  Li,F = LAW i − (Adiv + Aatm + As,F + Adif,F ) Li,H = LAW i − (Adiv + Aatm + As,H + Adif,H ) Values of each source are then long term evaluated and added above all sound trajectories and octave bands. We notice that geometrical attenuation Adiv 2 and atmospheric absorption do not change with propagation conditions3 and are expressed by:  Adiv = 20 log d + 11 Aatm = α(j)d/1000 in which d is distance in a direct line between source and receiver, α is air absorption coefficient expressed in dB/km: j 1 2 3 4 5 6

frequency [Hz] 125 250 500 1000 2000 4000

α(j) [dB/km] 0.38 1.13 2.36 4.08 8.75 26.4

All other attenuations are calculated in different ways for each meteorological condition. In favourable conditions, to determine ground surface attenuation As,F , we 2

It’s divergence attenuation of an isotropic source. They do not change with sound ray direction but the atmospheric absorption coefficient is given for a specific temperature (15◦ C) and for humidity of 70%. 3

CHAPTER 3. THE NMPB METHOD FOR ROAD TRAFFIC NOISE 13 need to distinguish three zones of propagation: close to the source, intermediate, close to the receiver. Attenuation will be calculated as a sum of three contributions considering that in central zone rays are less influenced by surface attenuation. In homogeneous conditions rays are straight so we don’t need to section zones. Formulas could be found in [7], however As depends on ground surface absorption G. Attenuation due to diffraction is caused by the lengthening of trajectory compared with direct line one. Moreover, diffracted trajectory is influenced even by ground absorption: in this case we have to separate calculation because a diffracted trajectory in homogeneous conditions could be a direct trajectory in favourable conditions. In fact, ray curvature could make visible a source and a receiver which couldn’t be in a straight trajectory (see figure 3.5). Therefore, calculation of diffraction for favourable conditions considers an equivalent height for barriers which is defined based on ray curvature.

Figure 3.5: Diffracted trajectory in favourable and homogeneous conditions

Chapter 4

Traffic and noise models implementation 4.1

IMAGINE project

IMAGINE project (Improved Methods for the Assessment of the Generic Impact of Noise in the Environment) was developed between 2003 and 2007: it was born as a scientific instrument of environmental policies support to Member States as a natural prosecution of HARMONOISE project. In this previous project harmonized emission models were developed for the entire Europe regarding railway and road noise pollution. IMAGINE’s aim was to produce the same methods to estimate airport and industrial noise requested by the END. Therefore, the common aim of both projects was to produce harmonized methods for the implementation of noise mapping in Europe. Project involved many partners: environmental agencies, research centres, infrastructures administrators, cars producers etc. and it was divided in different working packages with the following aims (see [10]): 1. produce guidelines for noise map data management (Work Package 1); 2. produce guidelines and examples for an efficient link between road circulation management and mapping for action plans (Work Package 2); 3. produce guidelines and examples of how and when measurements are useful to add to reliability of estimated noise levels (Work Package 3); 4. produce harmonized methods for airport noise (Work Package 4); 5. produce noise emission database for different vehicles class based on HARMONOISE methods and guidelines for vehicles not in standard classification (Work Package 5);

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CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION15 6. produce noise emission database for passenger and freight trains based on HARMONOISE methods and guidelines for vehicles not in standard classification (Work Package 6); 7. produce harmonized methods for industrial noise (Work Package 7); 8. make possible an easy and fast implementation of methods above (Work Package 8). This project tried to be a complete guideline to develop methods alternative to the ones suggested by the END: in fact, END methods are national models that are not always suitable to all European countries, anyway they represent the standard by law. So, IMAGINE methods are nowadays not implementable but the analysis carried out by working groups is still useful, even for this work with particular regard to comparison between mapping methods. The work package WP2 is very interesting because of having reviewed all kind of available traffic models and tested the capability of each one.

4.1.1

Use of traffic models to evaluate road noise levels

Deliverable 7 of IMAGINE project [11] provides guidelines for traffic modelling and indications about theoretical accuracy due to this kind of method. Principal parameters affecting goodness of traffic model are: traffic flow (i.e. number of vehicles), speed, speed distribution (how speed varies within the same vehicle class), accelerations and fleet composition (number of heavy vehicles). However, these parameters haven’t the same effect on sound levels: if a doubling of flow is needed to raise levels of 3 dB, it’s sufficient a variation of average speed of 30 km/h to have the same increasing on levels. Therefore, not all parameters have to be known with the same accuracy to obtain a fixed uncertainty; the following significance order is given (i.e. the order to be proceeded to improve accuracy): 1. speed and fleet composition; 2. flows; 3. accelerations and decelerations; 4. speed distribution; 5. data on low flow roads. These observations make easy to understand that a traffic model could improve speed accuracy: in fact, speed is usually taken equally to speed limits (not including congestions). Furthermore, IMAGINE WP2 carried out a Montecarlo simulation to evaluate which precision is necessary on speed and flow to achieve 0.5 dB or 1 dB

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION16 uncertainty on noise levels1 . In figure 4.2 simulation results are listed. Apart from accuracy of input data, there are different traffic models that are suitable to different situations and that differ on data requested and provided; we exclude demand models (based on survey about travellers characteristics and choices) and we concentrate on different network models which provide speed and flow on each link of transport network. These kinds of models are so classified: • static assignment models; • dynamic assignment models; • continuous models; • microsimulations models. In document [13], produced by WP2, methods have been described through SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) to understand which one fits better data available (or deliverable), aim or time of the study. In the following sections, models characteristics will be briefly presented. Static traffic assignment Static traffic assignment is the process of allocating trips in one or more trip matrices (origin-destination matrices or OD-matrices) to their routes (paths) in the network, resulting in flows on links. After calculation, it’s possible to obtain reasonable link flows and speed and also to identify congested links. The basic assumption is that travellers choose the route that minimize their travel cost (cost and time are directly correlated); however, this method is rarely able to distinguish vehicles classes, so after the assignment it’s necessary a class distribution on roads to produce vehicles categories. Moreover, static assignment is generally hour based: the origin-destination matrix contains for instance the trips of a peak hour. Capacities of the road network are expressed as number of vehicles per hour. As a result, the estimated flows are averaged per hour and so we need a daily distribution to estimate noise indicators.

1

Method used to produce noise from vehicles flows is HARMONOISE’s one, [12].

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION17

Table 4.1: SWOT analysis for static assignment Strengths Weaknesses Fast; All results are averages; Data collection relatively easy; Inaccuracy of results Easy-to-understand indicators; (especially speeds per link); Concise results; Usually night periods aren’t Results easy to use in GIS; modelled; Opportunities Threats Very common in local, regional Improvement of output for authorities; noise calculations requires More digitized or automatically a large effort; generated data will become available; Model results can cause a Relative simplicity of models ensures false sense of accuracy: results that new developments will usually seem detailed, but not be tried out in static models first; all indicators are significant; Dynamic traffic assignment Dynamic traffic assignment is the process of allocating trips in function of time in order to achieve time distribution of flows on network. Put into practice, it means that instead of a single OD-matrix we have one for each time interval (an hour or a quarter) that we want to analyse. Many models implementing dynamic assignment request that the temporal step of analysis is smaller than the shortest travel time over all network links (this means long run time in urban context). This assignment produces flow and speed as function of time and it makes also possible to distinguish between different vehicles classes. Table 4.2: SWOT analysis for Strengths Correct modelling of demand fluctuations; Possibility to model more accurately and correctly effects like congestion; Include multiple vehicle types; Results easy to use in GIS; Opportunities DTA models offer possibilities in high demand in impact assessment studies; More digitised or automatically generated will become available;

dynamic assignment Weaknesses Long run time; Few dynamic matrices for full day available; Inaccuracy of results Times step size determines accuracy of results; Threats It needs detailed input data; Difficult modelling technique;

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION18 Continuous models Continuous models treat vehicles as a unique entity idealized as a continuous fluid. Mathematical idea is that fluid delivery (i.e. flow per time reference) increases till maximum capacity of pipe (i.e. of road) and then decreases as shown in figure 4.1. This model is based on mathematical equations (solved sometimes numerically) to produce flow, speed and density through time (time step from 0.5 s to 10 s). This type of analysis has an high level of detail and it’s common in circulation management and not in noise studies: in fact, this model is suitable for long crowded roads because it doesn’t consider nodes.

Figure 4.1: Fundamental diagram of traffic flow (k is density; q is flow)

Microsimulation models Microsimulation models attempts to model the progression of individual vehicles: within each time step it uses a number of individual algorithms to generate decisions for all vehicles on the network. Position and speed of vehicles are updated at each step. There are two kinds of microsimulation models, the ones which consider a continuous network and the others that consider it as discrete (in this case the road could be occupied only if another vehicle is on the road). These models require more input data about network itself as signalized intersection data, numbers of lanes, width etc., so they are suitable to little local studies.

After this short review, it’s clear that some models fit better than others to calculation of a specific parameter and that not all models are suitable to urban noise mapping; in the following tables, properties of models are compared. Apart from observations about models types, some problems remain critical:

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION19 models often calculate single vehicles class so we have to pay attention to fleet composition; models are not reliable on low flow roads so we must decide how to treat them. These difficulties are greatest problems linking traffic model to noise model: that’s why WP2, in analogy with European Commission paper GPG, has written up a lot of toolkits to know previewed level of accuracy2 associated with a modelling choice. These toolkits will be used together with table in figure 4.2 to estimate uncertainty due to traffic model on noise levels calculated in Pisa.

Traffic Flows Speed Speed Distribution Acceleration Fleet composition

Table 4.3: Models & Output Static Dynamic Continuous + ++ + + ++ + + + +/+/+/-

Micro simulators +/++ ++ ++ +

++ available and reliable + available - not available

Table 4.4: Models & Contest and reference Time Static Dynamic Continuous Micro simulators Study Area Regional/National ++ + N N City + ++ N + Local motorways N + ++ + Local urban N 0 0 ++ Study period Peak Hour ++ + + + Day + ++ 0 + Year 0 0 N N ++ available and reliable + available 0 Neutral N not available/not common practice

2

In this case is not a decibel accuracy but a quality evaluation.

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION20

Figure 4.2: Montecarlo simulation results - boundary regions of accuracy on flow and speed to achieve 0.5 dB and 1 dB accuracy on sound power levels

4.2

Good Practice Guide

This document was written up by the European Commission working group Assessment of Exposure to Noise (WG-AEN) in a first draft in 2003 and its final version was published in August 2007 after a long international consultation process. The purpose of this Position Paper is to help Member States and their competent authorities to undertake noise mapping and to produce the associated data required by the END3 . This Position Paper dials with all possible problems applying the END and it tackles them with toolkits which suggest solutions and estimate associated accuracy. 3

It’s especially oriented to the first round of sources to be mapped.

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION21

4.2.1

Accuracy evaluation: toolkits

This paper guides authorities through noise mapping suggesting which modelling technique must be used to achieve a certain level of knowledge and accuracy: GPG carries on a deep discussion about all kinds of choices about the END requirements. In the toolkits (double-entries tables) different possibilities are listed with associated accuracy, degree of deepening and complexity (i.e. technical effort needed). However, not all tools evaluate accuracy in the same way: almost all evaluate accuracy in terms of decibel but some of them express more generally quality of the result. Legend of a general toolkit is shown in figure 4.3.

Figure 4.3: Toolkits Legend GPG suggests single steps accuracy but accuracy of final result is anyway tricky. In fact, uncertainty of each solution have to be combined to obtain total uncertainty but it’s not always known how them are related: they could add or subtract each others, besides it’s common practice to admit data belonging to independent Gaussian distributions and to do a square sum. However, this practice is not indicated in GPG which purpose is only to give a scale of goodness for each technique. We will calculate square sum but we will also carry on a comparison between measurements and calculated levels to determine discards distribution. GPG toolkits are shown in figure 4.4: each one is split in different tools according to available data. These tools will be taken into account for uncertainty calculation of Pisa road noise map.

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION22

Figure 4.4: Good Practice Guide step by step

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION23

4.3

European noise mapping experiences

4.3.1

Noise mapping of Pamplona agglomeration

Pamplona agglomeration includes 20 municipalities and it’s part of first round noise mapping because it takes up an area of 127 km2 with a total population of 280200 inhabitants. According to the requirements of the END, strategic map has been performed including industrial sources, aircraft, railway and a total of 7441 roads [14]. The area was very large so lots of iso-lines, curve lines and about 40000 elevation points were used to build Digital Terrain Model. Calculation was performed with software Cadna/A with a 10 m step grid (a smaller step was impossible because of width of the area). Other technical settings were: • only first order reflections considered; • maximum action radius of sources: 2 km; • building absorption 1 dB; • ground absorption G = 0.4. Input data for road acoustic map have been taken from national agency (only main roads) based both on measurements and predictions; data included both average speed and traffic composition. For the rest of not considered roads several criteria were implemented. One of them was to extrapolate from measurements to other similar roads. Another criterion was to evaluate the traffic density as function of the density of population to which the road is serving. In some cases great deviations were found but a pragmatic decision was finally taken and acceptable correlations with measurements were found. Average values of Hourly Average Intensity (HAI) expressed as vehicles per hour (v/h) for the six types of roads were as follows: Table 4.5: Pamplona road classification Type Name HAI(day) 1 Outlying roads 10 v/h 2 Quiet Residential Areas 50 v/h 3 Residential 100 v/h 4 Residential-Commercial 200 v/h 5 Commercial 350 v/h 6 Industrial 200 v/h The hourly distribution of traffic for day, evening and night periods were obtained from data continuously recorded at 66 measurement stations. The

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION24 percentages of heavy vehicles for different types of roads were obtained from real measurements in several roads and subsequent extrapolation to similar types of roads. Several noise measurements were carried out with the aim to configure the calculation parameters according to NMPB model, mainly to adjust the type of asphalts. Correlation between predicted and measured values was quite high (differences were less than 1 dB for receiver points near to emission lines). Nevertheless, differences increased with distance. Results showed percentage of people exposed to high levels: on day period 9% and 37% population are exposed to levels bands of 55-65 dB and 5060 dB respectively and 13% and 44% during night period.

4.3.2

Noise mapping of Scottish agglomerations

Scottish government disposed maps of Edinburgh and Glasgow agglomerations and major roads: these maps cover a very large area (only Glasgow agglomeration area is 766 km2 ) and include roads with more than 1000 vehicles daily passages. A specific traffic model, TMfS (Transport Model for Scotland) was implemented to perform calculation [15]: TMfS is a multi modal traffic model which provides peak hour flows for three periods (AM, intermediate, PM) with heavy goods vehicles percentage. Instead, streets with low flows inside agglomerations have been considered apart, assigning a typical flow. They implemented an automatic procedure based on arcs and nodes recognition to adapt network links of TMfS to real cartography; also road width has been automatically detected from cartographic data. Road surface and speeds have been taken from national database and corrected by local authorities. Digital terrain model was calculated by steps [16], considering both two dimensional cartography and LIDAR information about height: cartography resolution was 5 m (i.e. based on 1:10.000 cartography); instead height information was given rounded to meter. Moreover, they assigned a fixed minimum height of 5 m for all buildings. Regarding ground absorption they used information about rural areas to define hard ground. Scottish government (and whole United Kingdom) made a strong effort to adapt their indicators to European ones and to update maps already done; it’s nowadays managing action plans to be drawn up according the END.

4.3.3

Noise mapping pilot project in Portugal

Portugal environmental agency Apambiente carried out in 2004 a study about noise mapping in two different areas [17]: Carregado zone (Alenquer municipality and suburbs) and urban area Linda-a-Pastora in the municipality of Oeiras. Noise estimation was performed with software MITHRA

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION25 using NMPB method: meteorological conditions were deduced from local data, so probability of favourable conditions was taken as 30%. Different input/output resolution was chosen for each scenario: municipality scale scenario was reproduced with 1:10000 cartography and noise grid step was set to 18 m; instead urban scenario used 1:2000 cartography and an 8 m grid step. Moreover, municipality scenario considered second order reflections and urban one considers third order reflections. All road sources were considered with an action radius of 2 km and traffic data (flow and heavy vehicles percentage) were produced with automatic counters (from local highway administrator), manual counts and by comparatives analysis. Speed was estimated over all roads. A validation of noise maps was performed: average absolute differences between measured levels and estimated ones was 1.5 dB (with maximum deviation of 2.9 dB) over municipality scenario and 1 dB (with maximum deviation of 2.2 dB) over urban scenario. This good result is partially due to reduced number of considered streets (less than 50) and smallness of area. Furthermore, the area counts about 1650 inhabitants that are not very annoyed (27% are affected by diurnal levels higher than 60 dB and nocturnal ones higher than 55 dB). Nevertheless, this project shows Portugal willingness of improving environmental noise policies which didn’t exist before European Environmental Noise Directive (see [18]).

4.4 4.4.1

Tuscany case studies Florence road noise map

Published in February 2008, noise mapping of Florence municipality followed an agreement between municipality, province and ARPAT regarding noise fa¸cade calculation to provide PCRA. This study was carried out by U.O. IMREC of Florence ARPAT department and financed by municipality and province. Traffic flow evaluation method Traffic assignment was done through road classification (according PUT, Urban Traffic Plan) and using a standard flow per road class. Public Transport buses flow was assigned to links according data given by local transport administrator ATAF. Standard flow on links was estimated from following data: • automatic counts at 16 city gates (data from SILFI); • automatic counts at 16 ZTL gates (Limited Traffic Zones);

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION26 • counts campaign carried on by ARPAT on 25 streets (time period 48h); • manual counts on ZTL streets. Standard flows are here listed per road class:

Figure 4.5: Traffic flows assignment [19]

Sound Emission of vehicles classes Sound emission levels, as defined in the official method, don’t correspond to Italian fleet; therefore, it was necessary to transform real vehicles in light and heavy ones of NMPB database. Put into practice, this leads to the use of an acoustic weight of real flows to produce equivalent (noisily speaking) light and heavy flows. Weights were been calculated in a previous study carried out by ARPAT [20]: so the following emission values were assumed. Table 4.6: Emission values in Florence at 30 m distance and 10 m height Cars (C) Two wheelers (TW) Buses (B) Heavy vehicles (HGV) 28.6 dB(A) 30.1 dB(A) 37.3 dB(A) 33.4 dB(A) Then emission values were compared with NMPB ones (supposing 50 km/h speed) and a mathematical relationship was defined to obtain equivalent flows:  Ql,eq = nC ∗ 0.61 + nT W ∗ 0.87 Qp,eq = nB ∗ 0.29 + nHGV ∗ 0.12

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION27 Calculation reliability Before treating reliability, we want to underline calculation settings: traffic source was considered as a source on centre line with standard asphalt and road gradient was supposed null for all roads. Moreover, calculation took into account first order reflection and 10 m step grid is used. Fa¸cade levels were extrapolated subtracting 3 dB from grid interpolated values. Accuracy evaluation of diurnal levels was carried out and reported in [21] comparing results with 47 continuous measurements4 : these validation points were yet available from older campaigns (1995-2003). Results showed that only 22 measured points presented a difference from calculated values less than 3 dB. Main reasons of these errors are problems on 3D model realization, measurements reliability (too old to represent current situation) and mismatches between assigned traffic category and real flow on the road. This last problem results greater on night levels so that probability of residuals over 3 dB is enlarged. Therefore, accuracy of 3 dB was achieved only over 60% of control points.

4.4.2

First noise map of Pisa

First road noise map of Pisa was written up following an agreement with Pisa municipality for the PCRA. This action plan shall identify buildings where fa¸cade levels are higher than the ones established in the acoustic classification plan (PCCA) to manage reduction measures according to priority index defined by DM Ambiente 29/11/2002 in annex 1. In fact, maps simplify the identification of hot spots (i.e. critical areas) and the comparison with limits established by law. In September 2006 day levels map was produced[6] and at the beginning of 2007 the night levels map followed[22]. Moreover, exposed population was evaluated according to END exposure bands and European indicators (population from 1991 census). Source characterization Traffic flow on local network was assigned identifying homogeneous classes of streets and then a standard flow was assigned. Standard flow was based on the following data: • flow on province roads (report 2003, [23]); • flow from PUT of neighbouring municipality San Giuliano Terme; • hourly counts carried out by ARPAT (2005-2006); • routes and scheduling of urban and suburban public transport. 4

Measurements devices without direct control

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION28 Roads classification was based on suggested GPG classification (see tools 2.5, 4.5) and adapted taking into account PUT indications; to estimate night levels, a different coefficient per class was applied. Speed values were estimated assigning limits by law. Table in figure 4.6 summarizes these classes.

Figure 4.6: Standard Flow by road class Notice that identification of class 60 (Borgo, Corso Italia, Piazza dei Miracoli) and classes 40, 30-32 it’s immediately given by intersecting roads and polygonal shape of ZTL and 30 km/h zones; instead other classes needed a comparison with PUT maps. Roads classification is shown in figure 4.7.

Figure 4.7: Roads classifications [6]

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION29 As like in Florence, this work used an acoustic weighting to obtain NMPB equivalent light and heavy flow: same weight of ARPAT study [20] was used regarding cars, two wheelers and buses but not for heavy vehicles. In fact, heavy vehicles flow was set to zero on urban roads and it wasn’t weighted on other roads. Propagation and calculation settings Sound levels calculation was provided with software IMMI version 5.2 that implements NMPB method. All streets were inserted in the IMMI project with a good asphalt; streets were set as double direction (the one-way ones too) and total flow was placed on centre line. Terrain model was developed through regional cartography CTR 1:10000 using altitude points, iso-lines and curve lines, bridge altitude lines and hydrological lines. At the same way, walls and buildings were taken from regional cartography: it was necessary to modify some walls and building whose height was clearly mistaken (buildings minimum height was set to 3 m). To manage calculation, simplified method was used which takes into account reflections from surfaces: buildings were considered completely reflective instead of ground whose absorption was set to 0.5 (according to residential areas absorption suggested by GPG in tool 13.1). Meteorological correction considered only favourable condition (p = 1) according ISO 9613-2 and temperature of 25◦ C and humidity of 50% were set. Calculation was done dividing municipality in 200-300 m large zones and performing a grid with a step of 10 m. Uncertainty evaluation An accurate analysis was performed through GPG toolkits and other documents ([20] and [24]). Therefore, accuracy was evaluated for each step of noise mapping process and table in figure 4.9 summarizes theoretical accuracy for day period map (a similar one was made for night period map). So, a square sum was done and it resulted that day levels were affected by a global theoretical uncertainty of 4.3 dB and night levels of 4.6 dB. These uncertainty values were validated through a comparison with measurements: differences were calculated between modelled values and continuous (full day without direct control) and spot (an hour with direct control) measurements. Measurements were done by ARPAT to define Acoustic Clime in 2005-2006 or more generally to define road noise: day levels comparison was done on 54 continuous positions and 106 spot ones; night comparison was done on 51 continuous and 94 spot. Notice that night levels of spot measurements

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION30 were estimated according ARPAT guidelines [25] based on flow distribution per road class. Table from [25] follows, reporting correction factors for night levels and suggested measurement time interval for each road class: Road Type Urban or local road with low flow and low %HGV Inter-district or suburban road with low %HGV Main suburban roads and highways

Measure Days Mon.-Sat.

Time period 9.00 - 11.00

Correction 8 dB

Mon.-Sat.

10.00 - 12.00

6 dB

Thu.-Fr.

12.00 - 15.00

5 dB

Comparison evidenced that: • 90% day levels residuals were between 4.6 dB and -4.8 dB with median 0.11 dB; • 80% night levels residuals were between 5.3 dB and -4.1 dB with median 0.78 dB. By the end, we could say that residuals were distributed as previewed by GPG, i.e. a better result couldn’t be possible with available input data. Differences between calculated values and measured ones are shown in figure 4.8.

Figure 4.8: Residuals Distributions

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION31 Final considerations Improvement of accuracy is possible only with an improvement on traffic input data: already conclusions of first noise mapping report presented the possibility of a traffic model (recently bought by ARPAT) to estimate noise levels and a first example was shown in [26]. Furthermore, an improvement could be achieved with a better weighting: an estimation of local weights for two wheelers and buses was recommended after the first map. Finally, conclusions of that report emphasized that mapping is a dynamic instrument to be updated and verified: in fact, only if it’s updated, it could be a strategic help to protecting policies. So ARPAT continued working in this direction: this paper updates acoustic map with a traffic model and it’s part of the larger project of producing strategic map (including railway and aircraft noise).

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION32

Figure 4.9: Day model steps and accuracy [6]

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION33

4.4.3

Two wheelers sound emission evaluation

This study is part of a three-years master thesis of 2004 [27]: it’s a complementary study of the evaluation of acoustic clime in Pisa committed by municipality to ARPAT. The aim was to evaluate two wheelers contribution to environmental noise in some city streets: particular attention was given to ZTL streets of historical centre (here two wheelers circulate free in spite of cars). Moreover, it tried to determine average spectrum of two wheelers (divided between 50 cm3 and the ones higher powered). Sound levels were measured with spectrum analysers: levels were postprocessed to quantify two wheelers contribution. Single passages were identified within time history and total contribution was evaluated per type of street. After this analysis critical streets were evidenced to manage PCRA whose aim is an efficient noise reduction.

Achievements Counts and SEL calculation was made on 27 positions; counts per measurement point were between eight and twelve vehicles. From data acquired, it was possible to establish two wheelers contribution to global noise, i.e. to establish the maximum reduction cutting down on them: in ZTL noise levels could be lowered from a minimum of 1 dB to a maximum of 3.7 dB, instead on other streets contribution was less than a decibel. Measured SEL were extremely large distributed: SEL varied not only between streets with the same geometrical structure, but also within the same street (maximum variation was 3.4 dB). However, 50 cm3 power class was usually noisier than higher ones. This large dispersion of values was due to distribution of speed and acceleration which are also very broad in city context. In figure 4.10 is shown a section of summary table taken from [27] where SEL are listed. These values will be used in this paper to estimate sound emission for this vehicles class. Notice that measurements were done at an average distance of 3 m from the source and 4 m height above street surface; therefore, in following chapters, divergence correction will be applied to obtain emission values.

CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION34

Figure 4.10: Measured SEL from [27]

Chapter 5

A new approach to traffic assessment 5.1

Introduction

This work innovation consists in traffic assignment method: traffic model is implemented with software TransCAD version 4.8 (powered by Caliper Corporation and licensed to ARPAT). Utilization of this software started in 2006 to support data elaborations to be done for PCRA agreement with Pisa municipality; unfortunately, no traffic network analysis was carried out because there wasn’t enough time within dead line of PCRA. Traffic project restarts in September 2007 with this thesis: the first job has been to collect all data of first attempt, then to understand, update and enlarge data to mirror current situation. In fact, Pisa has been recently modified by: • insertion of many traffic circles; • development of north-east viability (Via Paparelli - Via Moruzzi); • definition of south ZTL (S.Antonio and S.Martino zones); • insertion of reserved lanes for public transport especially for new high mobility routes (LAM verde, rossa, blu). Moreover, viability next to railway station is actually influenced by construction of underground parking area. So, we have to decide how to model circulation between the following possibilities: 1. adopt old circulation to use previous measurements; 2. model temporary circulation to do calibration measurements; 3. model future circulation (according to approved project) to estimate future flow and impact. 35

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

36

We decided to take the third possibility because map ought to evaluate long term noise and mustn’t mirror old or temporary situations. During predisposition of traffic network, we tried to create a project as updated as possible providing necessary data and trying to obtain them from authorities: we collected counts of ZTL gates from PisaMo (mobility agency of Pisa) and we requested lights cycles to municipality which didn’t thought convenient to furnish them1 .

5.2

TransCAD characteristics

TransCAD puts together GIS capabilities and traffic flow management possibilities: it is useful both to traffic managers of administrations and to farms which want to know citizens routes. Any model producing flow on roads must start from a network and an Origin Destination matrix to know how many travellers go from origin Oi to destination Dj , that means to calculate flow path vector p or flow links vector f . The software provides many methods to evaluate matrix based on citizen characteristics (number of cars per family, income, population density. . . ): the matrix created during the first attempt was probably calculated in a similar way, but it considered too large OD areas making impossible to estimate traffic inside them. We decided to define a new matrix with more OD pairs. However, there were no data available about attraction or production of flow per area, so we used another estimation method. In fact, TransCAD is able to estimate matrix using following data: • flow counts on sample roads equally distributed on the network; • any OD matrix of desired dimension to initialize values; • road network of links and nodes with attributes requested by utilized assignment. Put into practice, this method creates a matrix according to counts and then it assigns flows to all other links through the selected method. Assignments implemented in TransCAD are all static assignments (the ones suitable to strategic purposes): • All or Nothing: all traffic demand is assigned to shortest path without capacity restraints; • STOCH assignment: calculation of path choice probability is performed by a proportion between travel times; 1 New lights cycles management was going to be furnished to municipality by a private agency but it wasn’t tested yet.

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

37

• Incremental assignment: at each step only a fraction of traffic demand is assigned through All or Nothing assignment, then travel times are updated as function of flow already assigned; • Capacity restraint: as previous one, but travel times are updated taking into account maximum capacity on links (this method may not converge to equilibrium); • User Equilibrium (UE): it use an iterative algorithm that reaches equilibrium when no traveller can improve own travel time changing route (it considers both traffic volume and capacity); • Stochastic User Equilibrium (SUE): as previous one, but travellers don’t know exactly network conditions and they have different perceptions of travel times; • System Optimum Assignment (SO): as UE, but it reaches equilibrium when total travel time is minimized (it’s not a realistic condition but minimize congestions). We selected UE method because is quite realistic and converges in a limited number of iterations: this method has a precise mathematical definition and we can demonstrate that utilized algorithm converges. This algorithm is Frank-Wolfe iterative process. In fact, at municipality scale, we need a model which takes into account congestions (i.e. capacity) and which converges to equilibrium solution; otherwise we could have long run time without reaching a correct solution.

5.3

User Equilibrium Method

Let’s precise the User Equilibrium concept: UE is usually called also DUE because it assigns a deterministic utility, i.e. cost, to links. Equilibrium conditions are expressed by the Wardrop’s First Principle presented in 1952: Theorem 1 (Wardrop’s First Principle) IF traffic demand is constant on project time, capacity restraints are not active and travellers behaviour is deterministic at full knowledge, THEN at equilibrium point, all paths between an OD pair with not zero flows have all the same travel cost, instead all the others have equal or higher costs. That is: p∗k > 0 ⇒ Ck∗ ≤ Ch∗ ∀h 6= k in which h, k are possible paths and C is cost. We want to show that p∗ is flow paths equilibrium vector if and only if it follows next equation, called variational inequality: C(p∗ )T · (p − p∗ ) ≥ 0 ∀p ∈ Sp

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

38

Demonstration In fact, if p∗ is an equilibrium vector only minimum cost paths are used and any other distribution p should use other paths with equal or higher costs so equation follows. Vice versa if p∗ verifies the equation it verifies also Wardrop equilibrium condition: in fact, if it would exist a positive flow on p which is not minimal, then we could obtain a vector p which wouldn’t respect Wardrop condition. For example, let’s consider to translate minimal flow on path k to path h: ph = p∗h + p∗k and pk = 0, then we would obtain C(p∗ )T p < C(p∗ )T p∗ this is against hypothesis. QED Let’s now consider a succession of project times t; at t it’s given the distribution pt , and relative costs vector C(pt ). At next time t + 1 flow path vector will change only if exists a path with a lower cost than the actual one. So flow vector evolves to equilibrium: C(pt )T · (pt+1 − pt ) < 0 Wardrop equilibrium condition may also be expressed as links (from node i to j) flow fij , instead of using flow path vector, through the following substitutions: P fij = k∈OD pk δij,k  ∗ T ∗ 1 if ij ∈ k =⇒ C(f ) · (f − f ) ≥ 0 ∀f ∈ Sf δij,k = 0 if ij ∈ /k Vectorf ∗ is called links flow equilibrium vector and it exists and it’s unique under following conditions. Theorem 2 (Existence) If C(f ) for all links is continuous then equilibrium vector exists. Demonstration To demonstrate this theorem, we have to introduce T (f ) = f − C(f ) defined in Sf . Let’s consider a generic f 0 and the vector f 00 ∈ Sf which minimizes distance to T (f 0 ):  T   00 f = min{H(f, f 0 ) = f − T (f 0 ) f − T (f 0 ) } in which H(f, f 0 ) is a scalar function only of f . Hessian of this function is a positive definite matrix (double of identity matrix), so H(f, f 0 ) is strictly convex.

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

39

Since Sf is closed, then it’s sure that f 00 exists and it solves minimum problem and it’s also virtual minimum for H(f, f 0 ), i.e it follows next equation: ∇H(f 00 , f 0 )T (f − f 00 ) ≥ ∀f ∈ Sf Moreover, thanks to strictly convexity minimum point is unique. Now let’s consider a function which associates f 00 to f 0 : this function is defined in Sf and its image is still in Sf so it’s continuous only if it’s continuous C(f ). Browers’s theorem asserts that a continuous function, with image contained in definition set, has a fixed point f ∗ : this point verify virtual minimum condition for H(f, f ∗ ). This condition is true for all f therefore we could apply it to f ∗ itself; considering that: ∇H(f ∗ , f ∗ ) = 2 [f ∗ − T (f ∗ )] = 2C(f ∗ ) we obtain variational inequality. QED Theorem 3 (Uniqueness) Equilibrium vector is unique if C(f ) is monotonic strictly crescent, that is if: [C(f1 ) − C(f2 )]T (f1 − f2 ) > 0 ∀f1 , f2 ∈ Sf Demonstration If we would have two different equilibrium vectors, then we could consider one as f and the other as f ∗ to write equilibrium condition or vice versa and we would obtain a contradiction with hypothesis of monotonic function: C(f2∗ )T (f2∗ − f1∗ ) = C(f1∗ )T (f2∗ − f1∗ ) + [C(f2∗ ) − C(f1∗ )]T (f2∗ − f1∗ ) which takes to C(f2∗ )T (f1∗ − f2∗ ) < 0. This leads to contradiction of equilibrium condition for f2∗ . QED The sufficient condition for cost functions monotonicity is that Jacobian matrix J[C(f )] is positive defined over whole Sf : elements of this matrix are partial derivatives of cost function of link i respect to flow of link j. If cost functions are dissociable, then J[C(f )] is diagonal: in this situation Jacobian matrix is positive defined if cost functions increase with flows. Therefore, whenever cost functions are dissociable, equilibrium vector f ∗ exists and is unique. Of course not dissociable functions might lead to unique vector but detailed

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

40

calculation should be performed. However, if Jacobian is symmetric, equilibrium vector is calculated as: I f ∗ C(x)dx f = min f ∈Sf

0

Moreover, if cost functions are dissociable we can express f ∗ as: X Z fij Cij (xij )dxij f ∗ = min f ∈Sf

0

ij

In the next section Frank Wolfe solution algorithm is explained.

5.4

Frank Wolfe algorithm

We can find equilibrium vector if we solve a constraint minimum problem minimizing the following quantity: X Z fij Z(f ) = Cij (xij )dxij ij

0

with next constraints: • not flows negativity: fij ≥ 0; • all transport demand must be assigned. The Frank Wolfe algorithm (published in 1956) minimizes Z(f ) looking for a descendant direction of Z(f ) (for a convex function descendant direction means minimum one). If at step k a solution f k is given, it looks for a distribution f k+1 closer to minimum. To find this distribution it’s necessary to expand linearly Z(f )2 : Z(f ) = Z(f k ) + ∇Z(f k )T (f − f k ) Notice that a minimal distribution for Z(f ) is minimal also for ∇Z(f k )T f ; because cost functions are dissociable, Jacobian is diagonal and so distribution is minimal for C(f k )T f . So descendant direction is identified by flow auxiliary vector fck defined as minimum for C(f k )T f : C(f k )T (fck − f k ) < 0 Then calculation of f k+1 is performed looking for a point between f k and fck : f k+1 = f k + λk (fck − f k ) 2

Calculation is taken from [28], [29] and [30].

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Put into practice, we look for λk Lagrange multiplier which minimizes Z(f k+1 ): fck minimizes total cost over the network so it’s the vector obtained from All or Nothing assignment. Therefore we can describe steps of Frank Wolfe algorithm: 1. we define an acceptance threshold ε and we make an All or Nothing assignment with costs of zero flows; 2. we update costs; 3. we calculate fck performing All or Nothing with updated costs; 4. we calculate λk performing bisection method: partial derivative of Z with respect to λ is performed in central point and we iterate on right portion with positive derivative or left otherwise; ∂Z(f k+1 ) X k k = Cij fijk+1 (fc ij − fij ) ∂λ ij

5. we update flow vector f k+1 ; 6. we decide how to stop algorithm with a convergence test through threshold ε: k+1 k f − f ij ij max <ε k ij fij if it’s not verified we increase k and turn back to step 2.

5.5

Transport network

Before assigning flows to network, we need to define it: a network is a collection of links and nodes with quantitative attributes. Transport network is the one whose quantitative attribute is cost function. We already said that Frank Wolfe algorithm converges to equilibrium if we use separable cost functions: this condition means that the function referring to link i is not influenced by flow on other links. Therefore, it’s a big approximation in urban context because we are asserting that travel time on a street is not influenced by flows of intersecting roads. With this hypothesis, cost function used by TransCAD is a function that provides travel time t:   v β  t = tf 1 + α c in which • tf is free flow travel time calculated as link length divided by free speed (law limit speed);

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• v is traffic volume assigned; • c is link capacity (maximum number of vehicles which can transit during time interval); • α, β are calibration parameters that are treated in following sections. So, within this model, cost is the same as time and is not influenced by other parameters (beauty of landscapes, monetary cost of travel etc.). To define OD matrix it’s necessary to distinguish OD nodes from intersection ones: OD nodes are called centroids and they are traffic accumulation points. Therefore, traffic on centroids is not necessary balanced and they idealize parking areas. Moreover, centroids aren’t directly linked to real network but through connectors that do not correspond to any real street: these connectors are in place of local streets whose traffic is not possible to be estimated. Local streets traffic depends on inhabitants distribution and cannot be calculated from the global network. Suburban, urban and inter-district streets have been inserted in the network excluding some local streets; nodes have been divided into centroids and intersection nodes. In figure 5.1 is shown the network with centroids and connectors emphasized. TransCAD allows inserting delays based on turn type (crossing, left or right turns) as a function of links classification and delays on specific nodes. These delays will be added to travel time t on path to make more reliable calculations. Following sections describe road classes parameters and used intersection delays.

Figure 5.1: Traffic Network: centroids and connectors

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5.5.1

43

Data collected from first step of TransCAD utilization

First attempt produced a network with major roads: principal parameters were also been defined like capacity, lanes directions, free speed (law limits), parking places presence etc. This network has been the base of the final one but it has been modified to mirror changes. In addiction counts data already available have been collected from different measurements campaigns:

detection technique manual manual laser detectors laser detectors video cameras

Table 5.1: Traffic data available year time temporal n◦ vehicles interval detail categories 1999 7.30-10.30 150 eight 2000 2006 2007 2006

7.15-9.30 24h 24h 24h

150 1h 1h 1h

unique five five four

n◦ and type of places 17 boundary roads 30 junctions 10 streets 1 street 7 gates

Obviously time interval and vehicles classes differences make immediately difficult to use these data: by the end, we decided to discard intersections data because it was impossible to obtain vehicles categories. All other counts have been reviewed to verify possible invalidation due to circulation changes. An OD-matrix was also prepared probably based on PUT data (published in 2000) providing movements between areas; anyway we decided to ignore it because we cannot achieve information about how it was created.

5.5.2

Road classification and input data

With the first part of the project a network was already prepared; roads were classified according PUT classification and following parameters were already assigned: • numbers of lanes and directions; • link type (a code relating to class); • free speed; • capacity per lane; • calibration parameters α and β. Calibration parameters were assigned changing default values of α = 0.15 and β = 4 to include the approximate effect of intersecting flows and intersection delays associated with a link. Therefore, these values have been

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taken as correct. These parameters have been estimated using values suggested by Highway Capacity Manual [31] for bidirectional roads: notice that HCM suggests values for a not dissociable cost function where v is flow of both links ij and ji. Values of HCM are: Table 5.2: Parameters from HCM Type of road α β divided highway 0.1 1 3.75 m large per lane 1 2.5 3.00 m large per lane 3 4 Moreover, classes have been enlarged to include highways with link type 0 and to assign different parameters within the same class according real context of roads. In fact, this first classification didn’t include 30 km/h zones and all ZTL, so the new one is summarized in following table. Table 5.3: Road link types and inputs Road type Link type Capacity Free Speed Highways 0 3600 90 Inter-district 1 2600 50 large roads 1300 50 Suburban 3 1600 70 roads 1500 60 Connectors 5 9999 30 1000 30 District and 6 1000 40 local roads 500 30 Inter-district 7 2600 50 roads 1300 50

α 0.1 2.5 2.5 1.5 1.5 1 1 3.5 3.5 3 3

β 1 4 4 3 3 1 1 4 4 4 4

This classification mirrors new speed values; in addiction capacity has been limited for ZTL local roads and connectors. Link classification is shown in figure 5.2.

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Figure 5.2: Traffic Network: road types

5.5.3

Intersection delays

Intersection delays have been inserted as turn penalties without including considerations about congestion, that is a fixed delay has been assumed. This kind of delay depends on link type because we considered that, apart from connectors, links with higher capacity are major roads respect to ones with a lower one. These delay values have been taken as the critical time gap needed in major road flow to do a specific movement from the minor one (left, right turn and crossing). Critical gaps are the ones described in High Capacity Manual [31] for stop signal (see figure 5.3): in this manual critical gaps are used to define a delay based on assigned flow and to perform an assignment with volume-dependent turning delays, so taking them as fixed produce an underestimation of delays. In fact, equation for volume dependent delay (seconds per vehicle) is the following: v     u vx 3600 2 u t vx cp,x cp,x  3600  vx d= + 900T ·  −1+ −1 + +5 cp,x cp,x cp,x 450T in which T is analysis time period and cp,x is potential capacity for movement x which is calculated as function of critical gap tc , follow up time tf (time needed to do single movement) and conflicting volume vc : cp,x = vc,x

exp(−vc,x tc /3600) 1 − exp(−vc,x tf /3600)

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Figure 5.3: Critical gap criteria for unsignalized intersections TransCAD provides a tool performing assignment with volume dependent turning delays: despite having inserted all inputs requested, tool breaks without assigning flows. This problem could not be solved, so fixed turn penalties have been used for both unsignalized and signalized intersections. In addition to turning delays, specific penalties have been inserted at ZTL gates to simulate low flows.

5.6

OD matrix calculation

As already mentioned, OD matrix has been estimated on traffic sample counts: this method needs that counts refer to the same time period. In fact, flow on network refers a specific time interval and, as presented in section 4.1.1, static models usually refer to peak hour. Moreover, available counts were done in the morning, so we decided to perform assignment on morning peak between eight and nine o’clock3 . Following sections describe how many counts have been used to estimate matrix and how vehicles categories have been treated. 3

An attempt to perform PM peak was done but calculations produced values similar to morning ones.

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With explained conditions it’s possible to estimate matrix from traffic flow considering that base OD matrix elements dbi differ from real demand di : dbi = di + θi . Moreover, also flows are not perfectly assigned so if H is the OD matrix, d is demand, we obtain estimated flow as: fb = Hd + ε. To estimate demand matrix we can solve an optimum constraint problem using Bayesian estimator d∗ which combine prior demand db with experimental flow fb:   X (di − dbi )2 X (fbj − P hlj dl )2 l  d∗ = min  + d≥0 var(θi ) var(εj ) i

j

This problem can be solved using an iterative algorithm called steepest descent or gradient descent method: b 1. we define an acceptance threshold ε and we initialize dk = d; 2. we calculate objective function D(dk ) and not restraint direction: g k = −∇D(dk ); 3. we calculate steep direction hk : ( k hi = gik if dki > 0 or gik ≥ 0 hki = 0

otherwise

4. we look for λk along hk direction: λk = min D(dk + λhk ) ¯ 0≤λ≤λ

¯ is maximum values which ensure not negativity of dk +λhk ; in which λ 5. we calculate new demand as dk+1 = dk + λhk and we increase k; 6. we decide how to stop algorithm with a convergence test through threshold ε: D(dk+1 ) − D(dk ) <ε D(dk ) if it’s not verified we increase k and turn back to step 2.

5.6.1

Sample counts

Many counts have been collected and performed: 52 of 70 available counts have been used to estimate matrix. In figure 5.4 a map of sample links is shown. However, counts are useful also to estimate day and night traffic distribution so many counts along the whole day have been done to validate the model.

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Figure 5.4: Traffic Network: sample counts Counts are collected in different ways: • manual counts: distinguishing cars, two-wheelers, heavy goods vehicles and buses; • video cameras: distinguishing cars, two-wheelers, light goods vehicles, heavy ones; • laser traffic counter: with Viacount we could have speed distribution and counts distinguished by vehicles length (following section will describe categories through length). Total counts are summarized in the following table:

n◦ counts 13 12 17 9 19

Table 5.4: Traffic Counts Period Time Method Sept 06/Sept 07 24h video cameras Oct 07/Feb 08 1h manual Nov 99 3h manual Jul 06 24h Viacount I Oct 07/Feb 08 24h Viacount II

Notes by PisaMo this project acoustic clime PCRA project this project

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5.6.2

49

Equivalent vehicles

TransCAD provides also multi modal assignment: to perform multi modal assignment, an OD matrix for each category is needed and there’s a tool which allows estimating multiple matrices. So, we tried to estimate heavy vehicles, cars and two wheelers matrices but heavy vehicles and two wheelers counts were too low to have e reliable assignment. Therefore, equivalent vehicles have to be used: as like as NMPB vehicles we have to define a weight but instead of acoustic power it must relate on road occupation. In fact, a road that reaches maximum capacity for x cars (nC ) will reach it with fewer heavy goods vehicles (hereinafter HGV) and with more two wheelers (nT W ); so we decided to define factors to convert two wheelers and heavy in passenger cars equivalent flow as shown in the following equation. neq = nC + 0.5 · nT W + 2.5 · nHGV Equivalent vehicles have been calculated from manual and cameras counts taking buses as HGV and light goods vehicles as cars. Counts performed with laser automatic traffic counters must be analysed with more attention: counters identifies vehicle length from axis distance, more exactly a laser beam starts from counter and see different lengths according distance between vehicle and counter (see figure 5.5).

Figure 5.5: Detector shadow on two-ways streets Therefore, different length boundaries values have been established for each lane (the one closer to counter has shorter boundary) according detector distance from road less than 0.5 m: Notice that automatic detectors could underestimate or give unreliable counts when closer lane traffic obscures opposite lane one. The resulting effect is an high number of short vehicles which corresponds to pieces of normal vehicles not completely detected: this means that opposite lane values are reliable only if traffic is quite low.

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Table 5.5: Automatic Counter categories from [32] category Length in closer lane Length in opposite lane two wheelers < 322 cm < 400 cm cars 322 − 750 cm 400 − 920 cm heavy vehicles > 750 cm > 920 cm

5.7

Flow and speed network assignment

We already explained how Frank Wolfe algorithm performs UE assignment, so we want to summarize requested inputs and show results. Inputs for OD matrix estimation and assignment are: • ID unique identifier of each link; • DIR code number which indicates if link is one or two way; • base OD matrix; in addiction we have to define for each direction available: • TIME free flow travel time on each link; • COUNT sample counts of peak hour; • CAPACITY, α,β; • PRELOAD. Pre-load is a fixed background link flow that is always assigned: public transport buses have been considered as pre-loads and assigned to links where they haven’t a reserved lane. Bus scheduling has been taken from CPT website and LAZZI website; urban lines routes have been identified by map on CPT website, instead suburban lines routes have been asked to bus drivers4 . Buses assignment has been performed in collaboration with Grad. A.Panicucci. Moreover, we added information including penalties. Resulting outputs for each direction follow: • equivalent flow; • travel time; • speed at average flow; • Volume to Capacity ratio. 4

In spite of written requests no data were furnished by CPT.

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Time and speed are the ones assigned at last iteration that is time corresponds to travel cost and speed is calculated as link length divided by time. It’s clear that at peak hour speed reflects congestions so also very low value (under 20 km/h) can be found near intersections or on traffic circles. Volume to capacity ratio V/C is level of congestion of links and it’s very useful to identify hot spots through creation of thematic coloured maps: TransCAD provides maps in which colours scale follows V/C ratio and thickness of links follows volumes. Such a map, produced on last assignment, is shown in figure 5.6.

Figure 5.6: Traffic Network Assignment

5.8

Model validation

Of course many traffic assignments were done before the last one: in fact many changes to network have been done before achieving a good balance between number of streets mapped and flows on them. In fact, if we consider too many streets, model is not able to assign flows correctly and we see effects of All or Nothing assignment: let’s consider two local streets going in the same direction (e.g. Porta a Lucca, CEP, Pisanova areas) connecting to the same major roads. In this scenario, model assigns all traffic to the shortest path and nothing to the other because flow is low. Therefore, such streets should be represented by only one connector or have an high flow. Other problems occur when too few roads are in the projected network (respect to the real one) and calculated flow increases extremely.

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Finally, we obtain a result that gives flow quite correctly: in figure 5.7 distribution of residuals is shown according uncertainty on noise levels from IMAGINE paper [11].

Figure 5.7: Residuals between modelled and measured flow At the end of the work it’s clear that we should have achieved a better result because only 73% of residuals differ less than 22% which was minimum accuracy to obtain levels uncertainty of 1 dB according [11]. We were unable to verify directly speed results because too few counts have been achieved with reliable speed: anyway we considered speed calculation correct for day period and we assumed speed limits for night period. Notice that speed calculation is conditioned by limits: more the flow is higher, more the speed is small but often free speed assumed is too low because many travellers don’t respect limits (especially on longer links).

5.9

Uncertainty evaluation

Traffic flow has been assigned to network for peak hour and equivalent vehicles. We want to evaluate decisions according IMAGINE toolkits to give an estimation of accuracy; principal aspects are: • Speed; • Acceleration; • Traffic composition; • Diurnal and long-time patterns; • Low flow roads;

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• Intersections; • Gradient. Figure 5.8 summarizes decisions and shows associated accuracy from [11]: notice that higher number of polygons angles means higher accuracy and toolkits provides triangle to hexagon degrees. If we assign an accuracy factor from 1 to 4 to different polygons we could say that adding all accuracy, total could be within 7 and 28 points: we achieved only 15 points so accuracy is quite poor. As already discussed, it would be unlikely to achieve 1 dB uncertainty over all links. To evaluate numerical accuracy we will consider GPG source-related toolkits (Toolkits 2-7). However, GPG toolkits include implementation of noise model so here only traffic model features will be analysed related to IMAGINE toolkits. The use of a traffic model on major roads to estimate flow is considered by GPG (Tool 2.5) having an accuracy of 0.5 dB. However, we have to remember that estimation on other roads will use first map flows so with 2 dB accuracy technique. Tool 3.5 provides accuracy for speed data: speed from traffic model has been used for day period on major urban roads; speed limits have been used for night period and for all other low flow roads. Therefore, 1 dB is associated to major roads day levels, instead night and low flow roads levels are affected by 2 dB uncertainty. Moreover, tool 6.1, regarding junctions, suggests a 1 dB uncertainty associated to ignore acceleration and deceleration. Toolkits 4, 5 and 7 regard traffic composition, road type, road gradient; these toolkits will be treated later because they are associated with modelling measures not explained yet.

CHAPTER 5. A NEW APPROACH TO TRAFFIC ASSESSMENT

Figure 5.8: Traffic model decisions

54

Chapter 6

TransCAD traffic output elaborations 6.1

Introduction

Traffic models are not born to perform noise calculation so many problems occur when output of traffic model is used as input of noise models. Principal reasons are explained in strategic maps guidelines [33] and here listed: 1. spatial resolution of traffic network; 2. temporal resolution of time period considered; 3. traffic data resolution (detail level). In fact, traffic network not only doesn’t cover all streets but also is mismatched with the real one. This means that road profiles should be verified before inserting in noise model and all other streets should be introduced (see section 6.2). Time reference period has been assumed as peak hour but we have to estimate LDEN and LN ight , so we have to extrapolate daily distribution to have average day evening and night traffic data (see section 6.3). Finally, traffic data don’t distinguish between vehicles categories, so we have to apply distributions to obtain NMPB vehicles (see section 6.4). All these problems are solved with a level of accuracy which is treated at the end of this chapter.

6.2

Spatial resolution

To complete network we merged the new one with the one of first acoustic map. Traffic values of first map (see section 4.4.2) were estimated on sample counts for Italian diurnal (6.00h-22.00h) and nocturnal (22.00h-6.00h) periods, therefore we considered day and evening traffic equal to Italian diurnal 55

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period. After having adapted periods, we substituted calculated roads to the old ones and we left the old flow on ignored links: this geographic merge is not easy because links don’t match exactly so a manual control on each zone has been done1 . All connectors and centroids have been deleted; figure 6.1 highlights old and new links.

Figure 6.1: Merging of new and old network It’s important to repeat that roads of first map didn’t model south ZTL (started in September 2006), so we adapted classes of links now included in this area and we modified flow on them; a lower flow has been assigned also to dead end streets whose traffic was unrealistic. Spatial resolution problem due to the traffic model causes streets to lie too close or under buildings on noise model: all streets lines have been modified to lie at the correct distance from buildings by means of GoogleEarth integrated with IMMI software. This procedure regarded not only streets control, but also building destination and other adjustments of the noise model, so it will be discussed later. 1 This geographic passage has been performed by arch. C.Chiari of ARPAT department with a sequence of automatic and manual operations on layers.

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6.3

57

Temporal resolution

Before merging traffic data with the old ones, we needed to produce traffic flow representative for day, evening and night. Many counts were performed with automatic detectors for an entire day: so, it was possible to derive flow distribution on sample roads and apply it on similar roads. Then we estimated coefficients to be applied to peak flow to derive flow on day, evening and night period. GPG suggested such values distinguishing between main roads and inter-district ones. GPG values are shown in figure 6.2.

Figure 6.2: Flow from Peak flow for different time periods from [5] These values have been taken as example and values for Pisa have been calculated from measurements; a new classification of roads has been done based on day-peak traffic ratio. In fact, flow time distribution doesn’t necessarily follow link type but it’s an easier classification between: 1. highways (no data available, major roads from GPG used); 2. suburban roads (5 samples); 3. urban inter-district and district roads (20 samples); 4. local and ZTL roads (13 samples). These classes are homogeneous not because roads have the same traffic profile, but more generally because percentages of day, evening, night flow respect to peak one are similar. Through these percentages flow periods coefficients are established for each class and time period; if α is percentage listed in the following table, average hourly flow for referenced period T is obtained as follows: QT = αT ∗ QP eak In figure 6.3 examples of distribution of classes 2, 3 and 4 are shown; hourly flows are divided by peak hour flow.

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Table 6.1: Percentage of flow for different time periods in Pisa Road class Italian day Day Evening Night 6.00h-22.00h 6.00h-20.00h 20.00h-22.00h 22.00h-6.00h 1 1 1 0.7 0.2 2 0.82 0.87 0.45 0.16 3 0.84 0.86 0.54 0.16 4 0.71 0.78 0.44 0.12

Figure 6.3: Examples of time distributions

6.4 6.4.1

Traffic data resolution From equivalent vehicles to real vehicles

Traffic model produces equivalent vehicles data that must be transformed into originally measured categories: cars, two-wheelers and HGV. Just like time distribution, also fleet composition has been calculated based on measurements applying the same road classification. Sample measurements were so distributed: 11 counts class 2, 23 counts class 3 and 14 counts class 4. Average percentages assumed are shown in following table. Road class 1 2 3 4

Two Wheelers (TW) 0% 8% 15% 45%

Cars (C) 80% 89% 82% 52%

∗ Bus not included

HGV∗ 20% 3% 3% 3%

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If we would have estimated total vehicles flow, then a simple ratio could be applied to obtain real vehicles: instead we have equivalent flow so a regression must be applied. In fact, if p is percentage, number of vehicles is given by Q = p ∗ T where T is total flow but we have to express T as a function of total equivalent flow E. E = 0.5 ∗ pT W T + 1 ∗ pC T + 2.5 ∗ pHGV T =⇒ T = E ∗ (0.5 ∗ pT W + 1 ∗ pC + 2.5 ∗ pHGV )−1 ≡ E ∗ β So flows per category are:   QT W = pT W βE ≡ γT W E Q = pC βE ≡ γC E  C QH = pHGV βE ≡ γHGV E γ values are listed in the next table for each road category. Road class 1 2 3 4

γT W 0 0.08 0.15 0.56

γC 0.62 0.89 0.85 0.63

γHGV 0.15 0.03 0.03 0.04

Notice that we never consider buses: as presented in previous chapter bus routes and scheduling are known as hourly flow so it would be wrong to estimate them. Buses flows are included in traffic models as pre-loads so we could, after the assignment, subtract them. Buses are considered as special category whose distribution is completely known: no time period correction is needed. Finally, we have an estimated flow per link at each reference period divided in four categories: two wheelers, cars, heavy vehicles and buses.

6.4.2

From real vehicles to NMPB light and heavy vehicles

An acoustic weight has to be assigned to each category to adapt NMPB emission values to real ones: we wanted to verify real emissions in Pisa taking as reference values the Florence’s ones. Following this approach, four measurements have been done: sound levels have been memorized together with time of single vehicles passages. Measurements were performed in different road classes, choosing streets with high public transport flow to evaluate their contribution. Of course single passages are very difficult to identify, however we identified 46 cars, 53 buses and 31 heavy vehicles passages. Instead, two-wheelers emission values have been calculated from ARPAT study described in section 4.4.3. For each single passage, SEL has been calculated from time history, then divergence and time correction has been applied to obtain emission values

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at 30 m distance and 10 m height (measurements were performed at 3 m distance and 4 m height). Notice that emission values of NMPB model are calculated in a free field condition but we were always near fa¸cades so we decided to subtract 3 dB. Same thing has been done with values furnished by [27]: we must consider that all categories have been averaged together, so about 250 two-wheelers passages have been considered. Following table shows measurements results compared to NMPB values2 : Table 6.2: Pisa emission values [dB(A)] Two-Wheelers Cars Buses Heavy vehicles Florence 30.1 28.6 37.3 33.4 Pisa 31.6 28.6 35.7 35.4 NMPB 30.7 30.7 43.7 43.7 Observed values shows that car emission value is equal to Florence one and so the same weight has been assumed; differences between bus and heavy vehicles are very low (considering that measured SEL3 vary about 4%) so we assumed the same weight (from higher values). Weights are here listed:  Ql,eq = QC ∗ 0.61 + QT W ∗ 1.2 Qp,eq = QB ∗ 0.16 + QHGV ∗ 0.16 Comparing Florence values with Pisa ones, principal differences are: • two wheelers are noisier in Pisa; this can be explained considering that Pisa links are very short and acceleration and deceleration influence is higher, anyway experience teaches that two wheelers are really noisier than cars so a coefficient higher than 1 is correct; • buses are noisier in Florence; this is probably due to Florence bus fleet that is older. Moreover, Pisa fleet counts many methane engine buses.

6.5

Accuracy of correction coefficients

Time coefficient, estimated like GPG example, is affected by two kind of uncertainty: • it considers week-end flow as weekday one and so 1 dB uncertainty is given in tool 2.3; 2

According to average speed, cars and two-wheelers have been compared to 50 km/h values, instead buses and heavy vehicles to 40 km/h values. 3 Explanation of this indicator is given in appendix A.

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• it’s possible that road categories are too simple and don’t mirror real time traffic distribution. However, measured time coefficients vary within the same class less than 25% so equivalent flow varies changing levels less than 1 dB. Distribution method to estimate traffic composition is the second option of GPG tool 4.5: uncertainty is estimated to be less than 0.5 dB. Moreover, if we consider also measurements we can observe that, within these classes, maximum varying parameter is cars percentage on local roads and it’s about 20%: therefore uncertainty expressed in dB is less than 0.8 dB. Considering that majority of network roads are classified as inter-district or district (which uncertainty is less than 0.5 dB), we can consider correct an uncertainty due to composition of 0.5 dB. Weight accuracy was estimated in first map work [6]: 1 dB accuracy was suggested but no clear explanation was given. Therefore, a test was managed to verify this accuracy: test was done measuring sound pressure levels and flows at the same time and then verifying estimated values. Flow and sound levels were averaged on a week and then light and heavy vehicles were calculated using weights on measured categories; day, evening and night measured flows have been inserted in the noise model and levels calculation on reception point (i.e. measurement position) has been performed. Measured and calculated values in Via di Gello (test location) are compared in following table: Measured Calculated Residuals

Day 65.4 dB(A) 66.6 dB(A) -1.2 dB

Evening 65.7 dB(A) 64.6 dB(A) 1.1 dB

Night 58.7 dB(A) 56.6 dB(A) 2.1 dB

Differences increase during night period because speed change: in fact assumed speed was equal to all periods instead the real one increases. Uncertainty of speed distribution has already been considered, so we accept 1 dB accuracy as suggested by [6]. Finally, we summarize all accuracy due to the use of TransCAD output as IMMI input: Problem Traffic Composition Emission values Week-end flow Long term values from peak flow

Solution apply distribution based on measurements weight from [27] and measurements same as weekdays apply distribution based on measurements

Accuracy 0.5 dB 1 dB 1 dB 1 dB

Chapter 7

Noise model implementation 7.1

Introduction

Noise mapping has been performed with prevision software IMMI version 6.3.1 (powered by Woelfel and distributed by Microbel). This version is very different from the one used for the first map. Not only bridge structures were added, but also calculation is implemented in a more efficient way. Moreover, it’s now possible to distribute calculation on many PC without manually sectioning the project (see section 7.3.3). All these modifications together with new streets and building elements induced us to create a new project: therefore, all cartographic elements have to be inserted and controlled. After that, calculation parameters have to be set: notice that calculation parameters ought to be a balance between accuracy and run time possibilities, so we try to improve calculation respect to previous possibilities. Nevertheless, we have to take into account that a maximum of three PC can be used to perform calculation. All assumptions and modelling choices will be described in the last section to evaluate accuracy of noise model.

7.2

3D model implementation

Regional cartography archive provides a digital reproduction with resolution 1:10.000 of the entire municipality and one with resolution 1:2.000 of residential areas. First map used only 1:10.000 cartography, instead we decided to cover municipality with a mixed cartography: 1:2.000 layers have been used to cover residential areas and 1:10.000 layers have been used to complete the remaining areas. Digital cartography provides much information but not all elements have the same accurate height: for example buildings and walls have a relative height, instead of altitude points and geodesic lines, rivers and sea lines 62

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which have absolute one. Moreover, it provides also street lines, foot-path lines, viaducts and bridge lines but their absolute height is not always reliable. In following sections we will analyse how elements have been treated.

7.2.1

Digital Terrain Model DTM

To create digital elevation model all possible elements have been used: that means all available lines and ground points with an absolute height greater than zero have been inserted. So principal elements are: • altitude points; • geodesic lines (iso-lines and curve lines); • streets and foot-path lines; • railway lines; • all hydrological lines (river Arno, sea, Navicelli canal and all watering canals). Of course random errors on cartography have been noticed and corrected, examples are here listed: • altitude points on viaducts and bridges with the same height of viaducts or bridges instead of ground one: points have been cancelled; • geodesic, railway, streets or foot-path lines and altitude points near buildings with gutter instead of terrain elevation: points and lines have been cancelled; • hydrological lines only on one river side: hydrological lines have been supposed equal to the other side and copied; • lines partly wrong because of previous errors: lines have been split and the correct part has been inserted. All these errors have been noticed and selected thanks to 3D IMMI viewer: the whole project has been analysed with 3D viewer which is able to select features from the project during the 3D view and it put them on a separate collection to be elaborated.

7.2.2

Bridges and viaducts

Bridges and viaducts lines have absolute height: these lines have been considered as suspended obstacles to propagation and classified into element class BRUCK. Width of each BRUCK structure can be set and also vertical

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barriers can be associated. However, not all viaducts or bridges are well defined in regional cartography; in fact, some bridges and viaducts have been created based on visual consideration. A list of principal created elements is given: • San Giusto bridge over railway lines; • Airport and Darsena FI-Pi-Li turn off; • Viaducts next to Bocchette bridge (Oratoio); • Via Livornese bridge over A12 (San Piero); • S.S.Aurelia bridge over via Conte Fazio.

7.2.3

Sound barriers and walls

In the first noise mapping project, all walls have been inserted including garden ones. Unfortunately, regional cartography gives relative height that is not always reliable; furthermore, it distinguishes between concrete, dry wall and fences but also this characteristic is not reliable. Therefore, we decided to consider and control only principal walls in the town: • city wall; • military zones walls; • botanical gardens walls; • prison walls; • Cottolengo walls; • river parapets. Moreover, almost all sound barriers have been considered: they are classified as wall whose absorption coefficient is very high. Some barriers height were available, others have been estimated by visual inspection with the aid of 3D viewer compared with 3D view of Pisa on Live Search Map website.

7.2.4

Buildings and streets: GoogleEarth utilization

Streets from created network have been inserted into the project adapting them to terrain height. This means that streets profile is the same of DTM: however, if too few nodes are inserted, streets could sink into the ground (see figure 7.1). All these situations have been identified with 3D viewer and modified inserting nodes to let the streets fit terrain profile.

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Figure 7.1: Streets adaptation to DTM Of course streets on bridge or viaducts don’t follow terrain profile but bridge one: software provides an automatic tool to adapt sound sources on BRUCK elements. This tool has been used ensuring that a sufficient number of nodes are included and well positioned in the considered street, otherwise this tool is not able to adapt the street correctly (see figure 7.2).

Figure 7.2: Streets adaptation to BRUCK Buildings have been originally taken from regional 1:10.000 cartography, instead of 1:2.000 as in the first map, because their relative heights are more reliable; moreover, detailed cartography includes balconies and other spreading elements that could complicate calculation. However, looking at the project, we realized that this layer lacks of many new buildings so we started using GoogleEarth to create new buildings. IMMI provides import of GoogleEarth georeferred maps on the project so that it’s possible to see

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both real photo and project elements. Therefore, we start adding new buildings (with height based on number of floors seen with Live Search Map) but during this process a new 1:2.000 layer was produced: we decided to insert new identified buildings from new layer merging with the old ones. Notice that we didn’t substitute the new layer with the 1:10.000 layer because this new layer is still less reliable than the other, especially in historical centre where no new buildings have to be added. In addition to new buildings identification, GoogleEarth tool has been used to update buildings use: regional cartography provides different buildings codes to identify their use but sometimes this could be different from real one (e.g. cemetery is coded as residential building). Codes distinguish between residential, industry, religious, and also other types of elements like hothouses, penthouses, huts that haven’t been considered. Finally, GoogleEarth has been used also to set streets on centre line: especially San Piero Fi-Pi-Li turn off and Via Moruzzi at the limits of the town were designed through this tool because no updated cartography was available. Other settings regarding buildings and streets are: • road surface has been considered as normal asphalt for all streets (except streets were absorbent asphalt has been recently installed); • circulation on road has been considered fluid and continuous with speed from traffic model for day and evening period and speed limit during night period (emission sound in these conditions is highlighted in figure 7.3); • minimum buildings height has been set to 3 m; • buildings with area less than 20 m2 have been cancelled.

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Figure 7.3: Sound emission levels for fluid continuous circulation

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68

Calculation settings

Calculation settings menu is divided into global parameters tab, elements parameters tab and calculation model tab. Elements whose parameters have to be set are streets, buildings and walls. Global parameters include temperature and meteorological conditions and other settings useful to manage population exposure without having data per building (it’s not this situation). Anyway, it’s more useful to analyse settings divided into sources and propagation settings because parameters of different tabs influence each other. Finally, calculation model issues will be treated.

7.3.1

Source settings and meteorological conditions

Most important setting regards how line source is transformed into point sources: as presented in chapter 3, French splitting methods are three (equiangular, constant and variable step), however this software implement only equiangular one. Equiangular step depends on receivers position and the equivalent length associated with point source is l ≤ αd, in which d is direct distance sourcereceiver and α is a parameter whose standard value is 0.5 but could be changed to increase number of sources. This distance criterion factor has been increased to manage free field calculation: in that case, otherwise, buildings distance would be too large and sources too much far from each other (point sources effect would be visible, see figure 7.4).

Figure 7.4: Different distances of receivers, effect on sources Moreover, each source has an action radius so that at a grater distance source is not considered: this radius has been set to 500 m for all streets apart from A12 highway whose radius has been set to 1.5 km. In fact, urban streets are very close to buildings and their noise cannot reach greater distance without loosing much power (due to reflection and absorption); instead, A12 is in free field condition and its power is very high so it could be heard at very

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long distances. Finally, source meteorological correction has been taken from GPG because no specific data were available. So probability of favourable conditions has been set to 50% − 75% − 100% respectively for day, evening and night periods. Other global meteorological settings regard temperature and humidity to calculate atmospheric absorption (a propagation issue): average temperature has been taken as 15◦ C and humidity as 70%.

7.3.2

Propagation: reflections and absorption coefficients

Only first order reflections have been considered: we tried to perform second order but run time increases exponentially and it was impossible to perform second order calculation on the entire municipality. Reflections have been activated for all vertical and horizontal elements, so an absorption coefficient has been chosen for buildings, walls and bridges. Tool 16 of GPG has been used and absorption coefficient has been set to 0.2 (i.e. lowering levels of 0.97 dB) for all elements apart from noise barriers whose coefficients have been expressed in dB and calculated from available measurements. Finally, ground absorption has to be set: tool 13.1 indicates 0.5 factor for residential areas and the whole municipality has been supposed to be like that.

7.3.3

Automated distributed calculation: segmentation

The program system of IMMI [34] provides the possibility of efficiently editing very large models and comprehensive grid calculations by means of the “AUDINOM – distributed grid calculation” module. The module is able to distribute calculation over several computers: in fact, project is automatically distributed over various computers and after calculation is completed, combination of partial grids is performed to obtain total grid. This module is the innovation, respect to the old version, which allows building and running a unique project over the entire municipality. Calculation has been distributed over three PC (maximum of licenses available): project is divided in several segments overlapping each other in all directions. Segmentation 9x9 has been carried out with an overlapping buffer of 500 m: buffers role is to include in the calculation area of each segment also other sources of neighbour segments whose power influences levels of considered segment. Notice that, for each segment, model doesn’t estimate noise levels in buffers but only inside the segment; after all segments are calculated, global grid is automatic assembled. Segmentation is a physical division of the project, so it’s possible that a segment does not include any sources in the buffers: here levels could not be calculated because there is no knowledge of other far sources. This problem

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occurred in the area of S. Rossore Park: however, traffic noise is not zero because A12 and S.S.Aurelia have high power levels that reach very far zones. To solve this problem a particular segmentation 3x5 with overlap of 1 km has been defined to include both areas and sources and only one segment has been calculated to cover this specific area. Finally, we must underline that few areas haven’t been calculated because sources action radius ended before reach them: in these cases interpolation has been performed.

7.3.4

Grid resolution

First map implementation performed a 10 m step grid: at the beginning of the project we would obtain 2 m grid step to improve resolution. Before trying this step over the whole project, we tested differences between grid steps: we consider a segment in the area of Tirrenia (about 2.8 km x 8.5 km large) where there is average buildings density but quite a plane DTM. Number of grid points and run time per step are listed in following table: Step 20 m 10 m 5m 2m

Points 60489 241674 964419 6019086

Time (minuts) 65 255 1020 6316

So 20 m and 10 m are very fast steps, instead 5 m grid needs about 17 hours and 2 m grid needs more than 4 days. Of course time for each point calculation is approximately constant (about 0.06 sec): so doubling step, time become a quarter. Particularly if we plot steps against time we notice a power trend.

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Of course slow steps are more reliable in urbanized context: a street is usually 6-8 m large, so 10 m step may calculate a point on the street or on the buildings instead 5 m one can calculate both. In following figures we compare grids in a specific area: we notice that 20 m grid is unable to represent noise correctly (an average area of 400 m2 is too large) instead other grids are quite similar. However, 10 m grid is unable to estimate correctly back fa¸cades levels, so it’s been excluded. From this example, we could assume that performing 2 m calculation over all municipality (15 km x 18.8 km) would take about 49 days: of course this time could be lowered considering segmentation on many PC; however, city context is more complicated so we decided to perform 5 m step grid (which should take 8 days on a single processor). Finally, 5 m step grid has been performed with AUDINOM on three PC (2 dual core with Windows XP, and a Pentium 4 with Windows 2000) and it takes about a week so a much longer time than expected.

Figure 7.5: Differences between grids levels (Diurnal)

Figure 7.6: Differences between grids levels (Nocturnal)

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72

Fa¸ cade calculation and population exposure

Population data of census 2001 have been elaborated by arch. C.Chiari and Grad. A.Panicucci to obtain number of inhabitants per building: in fact, they had to establish priority index defined by DM Ambiente 29/11/00 to assign action plans priorities. This elaboration is based on guidelines APAT [35] method b) which is the same suggested in deliverable 8 of IMAGINE project [36] (procedure F.b): population per building is taken proportional to the volume considering the use of each building (only residential ones) and knowing the number of inhabitants in a topological area (ISTAT cell). Notice that accuracy of this method could be low if no check of building use is performed, so an accurate control of schools and hospitals has been done. This elaboration considered residential buildings of new 1:2.000 layer. As we already explained that’s not the same used in IMMI project; however, population per building was there available and fa¸cade calculation have been performed only for these sensitive buildings (excluding industrial). This layer has been inserted into the project with population information together with codes to distinguish schools, hospitals from houses. Then IMMI performs fa¸cade calculation interpolating grids: levels have been calculated on a points ring around each building (2 m far from fa¸cade and 2.5 m far from each other). Maximum and minimum levels are evaluated to establish if a quiet fa¸cade1 is present. Then population exposure is calculated considering all inhabitants exposed to maximum level as established by the END. Notice that fa¸cade calculation according the END doesn’t consider reflected sound, but only incident one: reason is that people are not affected by sound reflected from the building, so 3 dB are subtracted by default when grids are interpolated. This subtraction suppose that fa¸cade are completely reflective: despite this, our settings are different because absorption coefficient is not zero. Therefore, for an incident level of 60 dB we have 59 dB reflected and total is 62.5 dB and so subtraction of 3 dB underestimate exposure of 0.5 dB.

7.5

Accuracy evaluation

Accuracy of noise levels is estimated with GPG toolkits. Source related toolkits not yet considered are toolkits 5 and 7 regarding road surface and gradient. Surfaces of Pisa streets are all normal asphalt apart from streets near two schools were absorbent asphalt is in use: so we assume an accuracy equal to 1 dB from tool 5.2 in which surface correction is based on physical properties. Road gradient is estimated from DTM because streets lines fit 1 By definition from annex 6 of DL 19/08/05 a quiet fa¸cade is one whose level is 20 dB lower than the higher fa¸cade level.

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terrain profile: accuracy of this method is greater than 0.5 dB (tool 7.1). Propagation issues are treated in toolkits 11-18. Toolkits 11-12 regard how cuttings and embankments are inserted in DTM: our cartography already includes them, so the once uncertainty is given by necessity to check them (accuracy greater than 0.5 dB, tool 12.1). Toolkit 13 regards how surface absorption is defined: we consider the whole municipality as residential area so tool 13.1 gives 1 dB accuracy. Toolkits 14-15 regards elements height: building height is known, so no uncertainty is given. However, height data from cartography comes from aerial photos so 1 dB accuracy should be considered according tool 15.2; instead, barriers (more generally walls) height is not always known correctly so we verified them by visual inspection. This last method accuracy is 1 dB according tool 14.2; however, considering that most walls are correct and known from regional cartography, we assumed 0.5 dB accuracy. Toolkit 16 regards absorption of vertical elements: we assumed suggested absorption coefficients so 1 dB accuracy has to be considered. Toolkits 17-18 regard meteorological condition but they express quality evaluation: we have low accuracy due to lack of local data. Finally, GPG gives qualitative accuracy of inhabitants estimation per building and per dwelling: as already said, according IMAGINE evaluation, our procedure is the best with available data. Furthermore, quality is good because we distribute population considering use of building: in addiction to this, 1:2.000 cartography distinguish between dwellings units so if we have a building which use is both commercial and residential, people are distributed proportional to residential volume.

Chapter 8

Noise mapping results 8.1

Noise road map

In previous chapters we explained how segmentation has been done and how we obtained global map. We will present all maps in appendix B: notice that there is a map for each period (day evening and night) and also one with LDEN indicator and one with Italian diurnal indicator. Here we show only maps of LDEN and LN ight indicators (figures 8.1 and 8.2). Together with whole municipality maps some detailed maps in appendix B show city centre, hospitals areas and an example of residential area. From whole municipality maps could be identified quiet area of S. Rossore Park on north-west and military areas on south-west; it’s clear that higher levels are due to highways A12 (north-south) and Fi-Pi-Li (east-west). Finally, we can observe that noise levels are quite low in south part of the municipality because population density is low too.

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Figure 8.1: Roads traffic LDEN levels

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Figure 8.2: Roads traffic LN ight levels (22.00-6.00)

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8.2

77

Population exposure to road noise

Population exposure has been evaluated according annex 6 of END: number of inhabitants exposed to levels of LDEN higher than 55 dB and LN ight higher than 50 dB has been plotted with 5 dB bands (see figure 8.3).

Figure 8.3: Exposed population to road traffic noise Moreover, inhabitants number with quiet fa¸cades is shown (see figure 8.4).

Figure 8.4: Inhabitants with quiet fa¸cades (road noise) Schools and university departments exposure has been analysed (see figure 8.5) divided into four categories according speech equivalent level: in fact, speech emission is about 55 dB and if incident sound is quite the same, people start to speak louder (open windows) and hearing becomes hard. Furthermore, hospital buildings distribution is shown in figure 8.6. We must consider that most exposed buildings belong to S. Chiara Hospital which is going to be dismissed, so exposure is going to be better. Finally, also percentage distribution is shown for Italian indicators in figure 8.7: this kind of graphic underlines also good levels to have an idea of the entire municipality.

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Figure 8.5: School buildings exposed to diurnal levels

Figure 8.6: Hospital buildings exposure

Figure 8.7: Distribution of exposed population

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8.3

79

Accuracy results

As already said, accuracy of maps presented is evaluated both theoretically and through measurements comparison. In following sections we summarize choices and we calculate global uncertainty, then we compare calculated and measured levels to validate model and to establish reliability of GPG.

8.3.1

Theoretical accuracy: global uncertainty calculation

We summarize choices divided into two groups: ones related to traffic model and more generally to input data which are different for TransCAD roads (flow from model), classified roads (flow from first map) and for night estimation; others related to noise model implementation. These choices and their accuracy are shown in following figure, together with toolkits used.

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Notice that in GPG is given the following explanation related to uncertainty of each tool: “The quantified accuracy statements presented within the toolkits represent the likely level of acoustic uncertainty introduced into the result by the use of that toolkit option, with a 95% confidence level. It must be noticed, that this represents the uncertainty of the total results only if all other input data is accurate. If there is uncertainty in any, or all, other input datasets, then the research concludes that total uncertainty in the receptor result level will be larger than any of the individual uncertainties.” This means that we have to establish how to calculate global uncertainty. We used the same method of the first mapping project: a square sum of uncertainties has been performed. In fact, distribution of possible errors due to a single choice is supposed to be independent1 from other ones: therefore superposition of distributions should be a Normal distribution with σt obtained squaring single uncertainties2 : sX σt = σi2 i

So global obtained theoretical uncertainties for different periods and streets are listed below: Table 8.1: Uncertainties of noise levels TransCAD Classified roads Day 3.1 dB 4.0 dB Night 3.5 dB 4.4 dB

8.3.2

Available measurements reliability

Comparison between estimated levels and measurements has been performed: measurements have been chosen within available ones including only traffic noise measurements. This means that we included only measurement campaigns whose aims were to detect noise from road infrastructures or environmental noise campaigns whose positions were far from other sound sources. Moreover, measurements have been excluded whenever traffic conditions have changed (new one way streets, new ZTL, new traffic circles. . . ). Of course also measurements have an uncertainty that ought to be included before analysing residual distribution. This kind of uncertainty is due to 1

Speed and flow for modelled roads are obviously not independent but their covariance is negligible. 2 Square sum corresponds to 95% boundary so 1.96σ values is calculated.

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instrumental chain, to operating conditions (time variability of source), whether conditions and residual sound (according ISO 1996-2, [37]): we can neglect all uncertainties but instrumental because measurements are just long term evaluated and flows are high so that operating conditions aren’t influential. Therefore, we have a σm equal to 1 dB and to acquire a 95% confidence level on residual distribution, 2 dB have to be squared together with global uncertainty on levels: q 2 σtot = σt2 + σm Global uncertainty at 95% of differences distribution is evaluated and listed below: Table 8.2: Uncertainty of residuals distributions TransCAD Classified roads Day 3.7 dB 4.5 dB Night 4.0 dB 4.8 dB Measurements include both continuous and spot positions because night levels for spot ones have been calculated according ARPAT guidelines [25]. Figures 8.8 and 8.9 show considered control points.

Figure 8.8: Measurements north positions

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Figure 8.9: Measurements south positions

8.3.3

Residuals distributions

We explained that roads have been modelled in two different ways and accuracy has been evaluated separately, so also residual distribution is supposed to be different for each kind of road. We extracted two data sets from available measurements: points in proximity of TransCAD modelled roads and ones performed on classified roads3 . So residual distributions have been calculated for each sample set: • 147 day and 110 night levels on modelled roads positions; • 63 day and 51 night levels on classified roads positions. We want to verify if 95% of samples are within theoretical values. In the first set 95% of diurnal and 92% nocturnal samples are within expected values (95% is within 4.3 dB). The first data set is quite large so we could also fit the distribution with a Normal distribution and analyse estimated parameters. Therefore, we applied following function to data set: x−b 2 c

f (x) = ae−(

)

being Normal function: 

N=√

1 − e 2πσ

x−µ √ 2σ

2

√ √ then σ = c/ 2 and 95% fitted confidence level is given by 1.96/ 2c. Fits are shown in figure 8.10. 3

Measurements near junctions of different types have been here ignored.

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Figure 8.10: Diurnal and nocturnal distributions of TransCAD data set Fits show that 95% confidence level value of diurnal distribution is 3.7 dB and night distribution fit returns 4.0 dB that are the same as previewed, so we have a perfect correspondence between fits and GPG forecasts. Instead of 4.5 dB, 4.7 dB is estimated value for day distribution on classified roads (for nocturnal it’s 5.5 instead of 4.8): however, this data set is quite small and distribution is not really a Gaussian because it’s not symmetric. Therefore, real dispersion is smaller than fitted one: in fact, 89% of diurnal data and 80% of nocturnal one are within expected values and only few measurement points are outside expected boundary. Moreover, distributions are not central: diurnal levels calculations of both sets seem to overestimate measured values (medians are −0.1 and −0.4); instead nocturnal sets have opposite medians (−0.8 and 0.8). This will produce a broader distribution on global levels: in particular for diurnal distribution we expect uncertainty similar to classified data set (it’s the larger one and it includes the other one); instead, nocturnal distribution will be broader than previous ones. Fitting global data (including ones at junctions) we obtain 4.3 dB for diurnal distribution and 5.5 dB for nocturnal that is what we were expecting (see fits in figure 8.11). We show also fits results in terms of estimated parameters, 95% convidence level and adjusted root square. Notice that b is average of the fitted curve and a is percentage of discards which are less than 1 dB: we can observe that levels of classified rods are overestimated (averages are negative), instead of TransCAD ones whose distributions averages are positive.

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Figure 8.11: Diurnal and nocturnal global distributions Table 8.3: Fits results Fit a b [dB] c [dB] 95% r-sq.

global

global

TransCAD nocturnal

classified

classified

nocturnal

TransCAD diurnal

diurnal

0.34 0.06 3.1 4.3 0.99

diurnal

nocturnal

0.28 0.49 4.0 5.5 0.99

0.42 0.07 2.6 3.7 1.00

0.38 0.84 2.9 4.0 0.98

0.31 -0.32 3.4 4.7 0.91

0.27 -1.03 4.0 5.5 0.78

Therefore, we can assert that GPG is able to predict accuracy in a reliable way but we have to consider uncertainties due to sound power levels and to flow period distribution. We want to underline that accuracy on calculated levels are the ones in table 8.1. Moreover, we could estimate global accuracy subtracting measurements uncertainty from obtained values: we have about 3.8 dB for diurnal values and 5.1 dB for nocturnal. Accuracy has been improved (see next section) on levels but main problem remains nocturnal values: in fact, we obtained that one method underestimates and the other overestimates producing a broader distribution. A possible improvement is to estimate correctly time coefficients for road traffic flow during night especially on classified roads (overestimation of values on classified roads is due to an overestimation of flow); furthermore, we could improve accuracy measuring speed during night because, especially on main roads, speed limits are often exceeded (that explains underestimation of night levels on TransCAD roads).

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85

Comparison with previous map results

If we compare first map with this one, we notice that residual distribution has been improved: in fact, distribution width is smaller. Notice that this improvement is not only due to traffic model (previous distribution is larger even than classified one), but also to a more accurate 3D model. Despite previewed nocturnal accuracy is quite the same of first map, residual distribution is better (see figure 8.12): that’s another evidence of a more accurate model.

Figure 8.12: Residual distributions Comparison between population estimations has been performed: at that time, IMMI version wasn’t able to manage automatic population exposure, so calculation was carried out with GIS software (Arcview 3.2). Grid values were assigned to residential buildings according nearest neighbour technique;

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then residential population was calculated based on volume proportion between the considered building and all residential buildings in that census zone (ISTAT cell, census 1991). Population exposure has been calculated both with Italian and European indicators: notice that LDEN and LN ight consider only incident sound. We prefer to compare distributions according diurnal and nocturnal Italian indicators (figure 8.13) because they are not corrected for reflections and so correspond to grid values. People exposure is more critical according new project: this fact could be due to walls modelling (garden walls not included), but also to more realistic traffic flows. Anyway, if we consider number of exposed citizens, this is quite the same because city population decreased. In fact, population exposed to nocturnal levels higher than 50 dB increased of 4100 inhabitants and to diurnal levels higher than 55 dB decreased of 5700 inhabitants.

Figure 8.13: Population comparisons

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8.5

87

Strategic noise map

Strategic noise map comes from superposition of other two maps to road one: aircraft noise has been calculated by Grad. Simonetti with software INM and railway noise has been calculated by Grad. Cerchiai and Panicucci with software SoundPlan. These maps have been exported as ASCII grids and then transformed into IMMI format so that energy sum could be performed according next equation:  La  Lr Ls L = 10 log 10 10 + 10 10 + 10 10 in which: La is aircraft noise level; Lr is railway noise level; Ls is streets noise level. We have to underline that railway noise hasn’t been calculated over the whole municipality, but only where it’s audible (i.e. till 2 km far from railway lines). Railway noise has important effects on west part of municipality where there is Torino-Palermo line, but it’s not dominant because it’s parallel to highway A12 and to S.S. Aurelia. Other important effects are due to PisaFlorence line on east part because many buildings are close to railway. Aircraft noise is prevalent on south-west part of municipality because departures and arrivals go along the same routes so that most populated part of the city is not affected. Of course road noise is prevalent in all other situations in the town. Accuracy of strategic map has been verified by Grad. Panicucci in [38]: global accuracy depends obviously on which source is prevalent (according next equation) but, if all sources are comparable, uncertainty is the biggest one between sources. p 100.2La ∆L2a + 100.2Lr ∆L2r + 100.2Ls ∆L2s ∆LG = 100.1La + 100.1Lr + 100.1Ls in which: ∆LG is global levels uncertainty; ∆La is aircraft levels uncertainty; ∆Lr is railway levels uncertainty; ∆Ls is streets levels uncertainty. Railway accuracy is the lowest and aircraft one is the higher: therefore, we can assert that near railway levels accuracy is about 4.5 dB (estimated in [38] using DEFRA4 position papers) and in all other locations it’s the same as road map. Strategic noise maps and single sources maps are shown in appendix C, here we show only LDEN and LN igth maps (figures 8.14 and 8.15). 4

Department for Environment, Food and Rural Affairs of United Kingdom.

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Figure 8.14: Strategic LDEN levels

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Figure 8.15: Strategic Night levels (22.00-6.00)

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8.5.1

90

People exposure to global levels

Global levels exposure graphics (see figures 8.16, 8.17) show that road noise is dominant and people distribution is quite the same as road noise: despite this, there are high annoyed people affected from aircraft and railway noise in proximity of these infrastructures. The evidence of this fact is decreasing of people with quiet fa¸cades. Finally, we should underline that annoyance due to different sources could be perceived in different ways [39]: many studies are going to identify indicators to evaluate correctly the contribution of each source so as they are perceived (see [40], Silence project [41]).

Figure 8.16: Inhabitants exposure

Figure 8.17: Inhabitants with quiet fa¸cades

8.5.2

Conflicts maps

In addition to people exposure, we elaborated conflicts maps: these maps are an efficient instrument to show critical areas. In fact, they show differences between law limits and calculated levels. Nowadays there aren’t limits for European indicators, so in Italy we elaborate conflicts maps for Italian indicators [42] whose limits are established not only by PCCA, but also by DPR n.142, 30/3/04 (road noise, [43]) and DPR n.459, 18/11/98 (railway noise, [44]); maps in figures 8.18 and 8.19 show these limits for Pisa

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municipality. Conflicts maps in figures 8.20 and 8.21 report differences. A detailed area is shown in figures 8.22 and 8.23 of the most critical area: in fact, south-east area is affected by Fi-Pi-Li, railway and aircraft noise so it’s a very annoyed area especially during night period. Other critical areas are a sector of S. Rossore Park, because of A12 highway noise in a very low limits zone, and south area next to railway and A12 which has low population density. These maps will be useful to manage action plans: PCRA establishes priority index based upon receiver type (school, hospital, house) and how much limits are exceeded, therefore is essential to know differences at receivers.

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Figure 8.18: Diurnal limits

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Figure 8.19: Nocturnal limits

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Figure 8.20: Diurnal differences between levels and limits

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Figure 8.21: Nocturnal differences between levels and limits

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Figure 8.22: Diurnal south-east differences between levels and limits

Figure 8.23: Nocturnal south-east differences between levels and limits

Chapter 9

Conclusions and developments This work has been carried out in Pisa ARPAT department and it produced Pisa road noise map according European Noise Directive 2002/49/EC which has been transposed in Italy by D.L. n.194, 19/8/05. A new approach for traffic flow estimation has been performed: TransCAD software has been used and an innovative technique has been developed to implement it in noise mapping procedure. We verified that noise mapping accuracy has been improved, according forecasts of Good Practice Guide and we produced new information about speed and flow starting from a limited number of measurements. Traffic flow measurements have been used to calibrate model and then to verify reliability of estimated flow. A procedure has been tested and verified to use passenger car equivalent flow into the traffic model and then to provide NMPB vehicles categories requested by the END. Moreover, traffic measurements have been used together with sound levels ones to calibrate noise model; finally, we validated noise estimation with available measurements and theoretical considerations. This calibrated model is able to predict both traffic and noise hot spots with good accuracy: actually it’s possible to obtain sound levels over the whole municipality with following accuracy: approx 66% of diurnal and nocturnal values are far from measured values less than 2.6 dB. These values confirm GPG suggested uncertainty: it means that future improvements should try to modify modelling methods which have an high influence over global uncertainty (i.e. the ones which contribute with more than 1 dB). In fact, it’s clear from GPG toolkits that low flow roads and especially speed estimation are critical problems. Possible improvement is to include night flow estimation into traffic model; this choice it’s possible only through traffic measurement campaigns on sample roads during night time. These future campaigns should include speed 97

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98

measurements: Pisa mobility agency has recently installed speed and flow detectors at town boundary streets, so they could provide input data for a more accurate traffic model in which free speeds might exceed law limits. In fact, principle obstacle to produce night flow model was lack of night data over a sufficient number of streets; furthermore, a night traffic model should use a transport network different from diurnal one, including only main roads (too low flows are not estimable). Another improvement of traffic model, which will produce an improvement on sound levels, is to include traffic lights cycles and use an assignment able to predict volume-dependent intersections delays. This would produce not only more reliable flows, but also more reliable speeds on links. Furthermore, if we consider traffic lights, we could also identify links with acceleration and deceleration. Traffic modelling could be also used for atmospheric emissions estimation in order to find a key synergy to tackle environmental issues as an holistic approach. The strong effort performed to produce an accurate 3D model will be useful for future studies: for example, if new census data will be available or new law limits, it will be sufficient to update values without building new projects. This work is also a useful instrument for future action plans because of provided results: it will allow authorities to test solutions in which traffic circulation change is a management tool to reduce noise levels. So this paper, together with the one of strategic map, will allow municipality to draw up action plans. Pisa has to draw up Italian local action plans PCRA, whose aim is to manage protecting measures and to promote policies oriented to noise lowering. Protecting measures for highly annoyed people (identified through conflicts maps) should be financed by responsible infrastructures. In fact, strategic noise map is essential for action plans because it allows identifying contribution of each source and establishing which infrastructure is responsible for specific limits overcoming. Finally this thesis produced data requested by the END to draw up European action plans: in fact, together with neighbour municipalities, Pisa is an agglomerate with more than 100.000 inhabitants and should therefore manage action plans. This thesis tackle both European and local policies providing technical management solutions.

Appendix A

Acoustics basics Sound Pressure level (SPL) Lp is a logarithmic measure of the root mean square (rms) sound pressure of a sound relative to a reference value. Sound pressure is the local pressure variation from the atmospheric room pressure caused by a sound wave at a given location and given instant of time. Let’s consider an infinitesimal volume V0 = dx dy dz with ρV0 mass moving with air at speed ux and a pressure gradient ∂p/∂x along x direction; therefore, force along x is given by pressure difference multiplied by surface: ∂p dx(dydz) ∂x This force is balanced by acceleration so motion law is given by: fx = −

dux ∂p V0 = −ρV0 ∂x dt Density doesn’t usually vary too much so we can write previous equation as: ∂p ∂ux = −ρ0 ∂x ∂t Moreover, if we consider all directions, we obtain Eulero equation: ∂~u ∂t However, motion induces a mass variation inside the volume but according mass conservation we can assert that time variation of density δ = (ρ−ρ0 )/ρ0 is related to speed divergence by: ∂δ/∂t = −div ~u. These variations are very fast so process is considered adiabatic so that following equation is given: grad p = −ρ0

∂δ 1 ∂p =γ = −γdiv ~u p0 ∂t ∂t in which p0 is static pressure and γ = cp /cv is specific heat ratio. Therefore, wave equation is calculated: ∇2 p =

1 ∂2p c2 ∂t2

99

APPENDIX A. ACOUSTICS BASICS

100

in which c is sound speed defined by: r p0 c= γ ρ0 RMS sound pressure is the root mean square of instantaneous one over a given interval of time. SPL is measured in decibels (dB) and reference sound pressure is 20µP a which is considered as the threshold of human hearing. So SPL is given from the following expression:  2  prms Lp = 10 log p20 At the same way, sound power level is given for a reference sound power of 10−12 W:   W LW = 10 log W0 Sound power level (measured one meter far from source) is related to sound pressure level so that for a point source in free field condition and for room temperature (i.e. when ρ0 c is 400P a · s/m) SPL is given from: Lp = LW − 10 log 4π instead for a linear source: Lp = LW − 10 log 2π However, sound perception varies with frequency that is SPL at different frequencies is heard at different loudness: therefore, it’s been defined equal loudness contours (expressed in Phon) which are a family of curves functions of frequency. In particular the 40 dB curve is called A-weighting and it’s used to correct SPL to mirror real human sensation. So, environmental sound pressure level is usually1 expressed in dB(A) where A-weighting correction is applied to each octave band. Environmental noise is estimated through A-weighted equivalent level which corresponds to a constant hypothetical source whose sound energy is the same of real time-varying sound:  Z T 2  pA (t) 1 LAeq = 10 log dt T 0 p20 Notice that T is time reference interval and it’s usually an hour so we speak about LAeq,h . Another important indicator is Sound Exposure Level SEL which is used 1

Aircraft noise is weighted with 100 dB countours.

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101

to identify contribution of single events: it’s the level that the event would assume if all his energy would be concentrated in one second SEL = LAeq + 10 log

Te 1sec

in which Te is real event time length. Law limits in Italy are expressed as average diurnal and nocturnal values of LAeq,h : this means that we estimate average hourly equivalent level over diurnal period (6.00:22.00) and nocturnal period (22.00:6.00). " # 1 X LAeq,hi LD = 10 log 10 10 16 i

"

LN

1 X LAeq,hi 10 10 = 10 log 8

#

i

in which i varies along hours. The END instead establishes different indicators: in addiction to LN called LN ight , it defines LDEN as a more complete indicator defined by the following expression (time periods adapted according DL n.194, 19/08/05):   Lnight +10 Lday Levening +5 1 10 + 8 · 10 10 14 · 10 10 + 2 · 10 LDEN = 10 log 24 in which Lday , Levening , Lnight are the A-weighted long-term average sound level as defined in ISO 1996-2: 1987, determined over all day (6.00:20.00), evening (20.00:22.00) or night (22.00:6.00) periods of a year. These levels should be estimated at 4 m height and they should consider only incident sound, this means subtraction of 3 dB must be done measuring fa¸cade levels.

Appendix B

Road noise maps We show whole municipality maps of day, evening and diurnal time period (figures B.1-B.3). Then detailed maps of LDEN and LN ight levels are shown: • City centre zone with principal roads and ZTL zones (figures B.4 and B.5); • Porta a Lucca residential area at the north limits of the town (figures B.6 and B.7); • S. Chiara hospital area (figures B.8 and B.9); • Cisanello hospital area (figures B.10 and B.11). Notice that S. Chiara hospital is going to be dismissed so whatever kind of buildings will be built, it will lay in a quiet area only if actual hospital walls (or similar) would not be destroyed: in fact, Via Bonanno has high power levels and it might affect the area. Moreover, Cisanello hospital lies actually in a quiet area but with the future enlargement a new viability is previewed: therefore, administrations should pay attention to position of hospital rooms and major access roads.

102

APPENDIX B. ROAD NOISE MAPS

Figure B.1: Road traffic noise Day levels (6.00-20.00)

103

APPENDIX B. ROAD NOISE MAPS

Figure B.2: Road traffic noise Evening levels (20.00-22.00)

104

APPENDIX B. ROAD NOISE MAPS

Figure B.3: Road traffic noise Diurnal levels (6.00-22.00)

105

APPENDIX B. ROAD NOISE MAPS

Figure B.4: Road traffic noise LDEN levels

Figure B.5: Road traffic noise LN ight levels (22.00-6.00)

106

APPENDIX B. ROAD NOISE MAPS

Figure B.6: Road traffic noise LDEN levels

Figure B.7: Road traffic noise LN ight levels (22.00-6.00)

107

APPENDIX B. ROAD NOISE MAPS

Figure B.8: Road traffic noise LDEN levels

Figure B.9: Road traffic noise LN ight levels (22.00-6.00)

108

APPENDIX B. ROAD NOISE MAPS

Figure B.10: Road traffic noise LDEN levels

Figure B.11: Road traffic noise LN ight levels (22.00-6.00)

109

Appendix C

Strategic noise maps Strategic maps are energy sum of three maps: aircraft noise, railway noise (both 10 meters step grids interpolated to obtain 5 m) and road noise. We show day, evening and diurnal strategic maps (not shown in previous chapters) in figures C.1-C.3 and maps of aircraft (figures C.4-C.6) and railway noise (figures C.7-C.9) in terms of Diurnal, DEN and Night levels.

110

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.1: Strategic Day levels (6.00-20.00)

111

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.2: Strategic Evening levels (20.00-22.00)

112

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.3: Strategic Diurnal levels (6.00-22.00)

113

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.4: Aircraft Diurnal levels (6.00-22.00)

114

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.5: Aircraft LDEN levels

115

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.6: Aircraft LN ight levels (22.00-6.00)

116

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.7: Railway Diurnal levels (6.00-22.00)

117

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.8: Railway LDEN levels

118

APPENDIX C. STRATEGIC NOISE MAPS

Figure C.9: Railway LN ight levels (22.00-6.00)

119

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