traffic model uncertainty for noise mapping

Universita di Pisa

Facolta di Scienze Matematiche Fisiche e Naturali

Corso di Laurea Specialistica in Fisica Applicata

Anno Accademico 2007-2008

Tesi di Laurea Specialistica

Traffic model uncertaintyfor noise mapping

Incertezza associata all’uso di modelli di trafficoper la redazione di mappe acustiche

Candidato RelatoreELENA ASCARI Chiarissimo Prof. G.LICITRA

ii

Abstract

After the European Parliament published the Environmental Noise Direc-tive 2002/49/CE (END) and the implementation by Member States in theirown legislation, they had to use the same evaluation methods to analysenoise pollution and the same indicators suggested by the END (LDEN endLNight). The aim of the END is an international comparison between Eu-ropean countries, through strategic noise mapping and action planning.Moreover noise mapping is nowadays the principal way for Italian publicadministration to manage noise pollution and to draw up acoustic mitiga-tion plans. So municipalities ought to have reliable maps and dynamic mapseasily modifiable, to mirror changes over traffic flow circulation. Followingthis approach, this work is focused on setting up Pisa road noise map basedon a traffic model.Because of lots of inputs data requested, it’s necessary to evaluate accuracyof final product based upon goodness of inputs. This evaluation has beencarried out by the European commission and officially published in 2007 asthe Good Practice Guide. This paper will apply this guide to verify relia-bility of suggested accuracy in noise mapping and to evaluate uncertaintyassociated with predicted noise levels, when measurements are used to vali-date mathematical models.

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Thanks

I want to sincerely thank all members of U.O. IMREC (Mobility Infrastruc-tures, Electric and Communication Networks) of Pisa ARPAT departmentfor help, patience and willingness during my stay: special thanks to architectC.Chiari for her GIS knowledge, to Grad. M.Reggiani to teach me softwareIMMI, to Grad. M.Cerchiai, D.Simonetti and A.Panicucci to make possiblethe presentation of strategic map. Moreover very special thanks to FabrizioBalsini to teach me and help me using instruments to perform both soundlevels and flow measurements. I want also to thank PhD. G.Memoli alwaysplaced at my disposal for explanations. Finally thank to Prof. G.Licitraand Prof. P.Gallo which support me and this work.

Contents

1 Introduction 1

2 Objectives 3

3 The NMPB method for road traffic noise 53.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Guide du Bruit: sound emission DB for light and heavy vehicles 53.3 NMPB-Routes-96: meteorological correction . . . . . . . . . . 93.4 Source characterization . . . . . . . . . . . . . . . . . . . . . 113.5 Attenuations due to propagation . . . . . . . . . . . . . . . . 12

4 Traffic and noise models implementation 144.1 IMAGINE project . . . . . . . . . . . . . . . . . . . . . . . . 14

4.1.1 Use of traffic models to evaluate road noise levels . . . 154.2 Good Practice Guide . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.1 Accuracy evaluation: toolkits . . . . . . . . . . . . . . 214.3 European noise mapping experiences . . . . . . . . . . . . . . 23

4.3.1 Noise mapping of Pamplona agglomeration . . . . . . 234.3.2 Noise mapping of Scottish agglomerations . . . . . . . 244.3.3 Noise mapping pilot project in Portugal . . . . . . . . 24

4.4 Tuscany case studies . . . . . . . . . . . . . . . . . . . . . . . 254.4.1 Florence road noise map . . . . . . . . . . . . . . . . . 254.4.2 First noise map of Pisa . . . . . . . . . . . . . . . . . 274.4.3 Two wheelers sound emission evaluation . . . . . . . . 33

5 A new approach to traffic assessment 355.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.2 TransCAD characteristics . . . . . . . . . . . . . . . . . . . . 365.3 User Equilibrium Method . . . . . . . . . . . . . . . . . . . . 375.4 Frank Wolfe algorithm . . . . . . . . . . . . . . . . . . . . . . 405.5 Transport network . . . . . . . . . . . . . . . . . . . . . . . . 41

5.5.1 Data collected from first step of TransCAD utilization 435.5.2 Road classification and input data . . . . . . . . . . . 435.5.3 Intersection delays . . . . . . . . . . . . . . . . . . . . 45

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CONTENTS v

5.6 OD matrix calculation . . . . . . . . . . . . . . . . . . . . . . 465.6.1 Sample counts . . . . . . . . . . . . . . . . . . . . . . 475.6.2 Equivalent vehicles . . . . . . . . . . . . . . . . . . . . 49

5.7 Flow and speed network assignment . . . . . . . . . . . . . . 505.8 Model validation . . . . . . . . . . . . . . . . . . . . . . . . . 515.9 Uncertainty evaluation . . . . . . . . . . . . . . . . . . . . . . 52

6 TransCAD traffic output elaborations 556.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556.2 Spatial resolution . . . . . . . . . . . . . . . . . . . . . . . . . 556.3 Temporal resolution . . . . . . . . . . . . . . . . . . . . . . . 576.4 Traffic data resolution . . . . . . . . . . . . . . . . . . . . . . 58

6.4.1 From equivalent vehicles to real vehicles . . . . . . . . 586.4.2 From real vehicles to NMPB light and heavy vehicles . 59

6.5 Accuracy of correction coefficients . . . . . . . . . . . . . . . 60

7 Noise model implementation 627.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627.2 3D model implementation . . . . . . . . . . . . . . . . . . . . 62

7.2.1 Digital Terrain Model DTM . . . . . . . . . . . . . . . 637.2.2 Bridges and viaducts . . . . . . . . . . . . . . . . . . . 637.2.3 Sound barriers and walls . . . . . . . . . . . . . . . . . 647.2.4 Buildings and streets: GoogleEarth utilization . . . . 64

7.3 Calculation settings . . . . . . . . . . . . . . . . . . . . . . . 687.3.1 Source settings and meteorological conditions . . . . . 687.3.2 Propagation: reflections and absorption coefficients . . 697.3.3 Automated distributed calculation: segmentation . . . 697.3.4 Grid resolution . . . . . . . . . . . . . . . . . . . . . . 70

7.4 Facade calculation and population exposure . . . . . . . . . . 727.5 Accuracy evaluation . . . . . . . . . . . . . . . . . . . . . . . 72

8 Noise mapping results 748.1 Noise road map . . . . . . . . . . . . . . . . . . . . . . . . . . 748.2 Population exposure to road noise . . . . . . . . . . . . . . . 778.3 Accuracy results . . . . . . . . . . . . . . . . . . . . . . . . . 79

8.3.1 Theoretical accuracy: global uncertainty calculation . 798.3.2 Available measurements reliability . . . . . . . . . . . 808.3.3 Residuals distributions . . . . . . . . . . . . . . . . . . 82

8.4 Comparison with previous map results . . . . . . . . . . . . . 858.5 Strategic noise map . . . . . . . . . . . . . . . . . . . . . . . . 87

8.5.1 People exposure to global levels . . . . . . . . . . . . . 908.5.2 Conflicts maps . . . . . . . . . . . . . . . . . . . . . . 90

9 Conclusions and developments 97

CONTENTS vi

A Acoustics basics 99

B Road noise maps 102

C Strategic noise maps 110

Chapter 1

Introduction

After the European Parliament published the Environmental Noise Direc-tive 2002/49/EC (hereinafter END, [1]) and the implementation by MemberStates in their own legislation, they had to use the same evaluation meth-ods to analyse noise pollution and the same indicators suggested by theEND (LDEN and LNight). The aim of the END is an international compar-ison between European countries especially using strategic noise maps1 andaction plans2. Member States have to ensure that maps and action planswill be write up in these different situations: agglomerations with more than250.000 inhabitants, major roads with more than 6 millions vehicles per year,major railways with more than 60.000 trains per year and major airportswith 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 in-habitants, major roads with more than 3 millions vehicles per year, majorrailways 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 whichtransposes the END, establishing endorsements for defaulting authorities [2].However, noise mapping is nowadays the principal way for public adminis-trations to manage noise pollution even in context different from the onesimposed by the END: in fact, an acoustic map is not only a photo of theacoustic conditions of a single infrastructure, but also a dynamic instrumentfor urban planning.Especially in Italy, strategic noise mapping is the first step of Italian ac-tion plans (hereinafter PCRA) which must be drawn up by municipalitiesin order to attempt DM Ambiente 29/11/00, [3]. Authorities have to verifyif noise levels exceed limits established by local acoustic classification plans(hereinafter PCCA) defined in DPCM 14/11/97, [4]. Therefore, using re-

1A map designed for the global assessment of noise exposure in a given area due to allsources.

2Plans designed no manage noise issues and effects, including noise reduction.

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CHAPTER 1. INTRODUCTION 2

sults of an acoustic map, authorities may set up strategic measures whichcould 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 thesame time dynamic maps easily modifiable to mirror changes in traffic flowcirculation. Following this approach, this thesis is focused on setting up Pisanoise map based on a traffic model.However, it’s always been critical to map very large zones as an entire mu-nicipality, not only because calculation models may take long run time, butalso because we need to collect lots of input data not always available.We have to do many assumptions and to estimate as many parameters tocarry out acoustic maps (and more have to be done using traffic model):therefore, we evaluated goodness of final product based on detail and accu-racy of inputs, according evaluation carried out by the European commis-sion and officially published in 2007 as the Good Practice Guide (hereinafterGPG)[5]. This document is fundamental for this work because it providesaccuracy for each modelling choice.This thesis will present the noise map for road source in the municipalityof Pisa using a traffic model and it will evaluate the uncertainty associatedwith estimated noise levels: this work is continuous with previous acousticmap of Pisa written up in 2006 [6]. Although it wants to improve the levelof deepening and reliability making a different assignment of traffic flows;this approach should be an improvement of the assignment based on roadclassification (fixed flow for each class) used for the first map and it’s fore-casted by GPG [5] and suggested also in [6].Both software TransCAD (traffic flows calculation) and IMMI (noise levelcalculation) will be presented with special attention to methods implementedand to all elaborations needed to use the output of the first as input of thesecond. Moreover, this work is part of the broader project of strategic noisemap: so results of strategic map will be presented taking into account alsorailway and airport noise.Strategic noise map is a project of regional environmental agency ARPATcarried on in Pisa department by U.O. IMREC (Mobility Infrastructures,Electric and Communication Networks Unit): therefore this work has beendone in ARPAT department.

Chapter 2

Objectives

The aim of this study is to write up the road noise map of Pisa municipalityusing a traffic model for vehicles flows calculation and its validation throughmeasurements to verify reliability of accuracy suggested by GPG.Therefore, we want to evaluate uncertainty associated with residuals be-tween measurements and calculated levels to show accuracy of used mod-elling 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 takinginto account recent modifications).

After having number of vehicles per road, we need to estimate sound powerlevels: unfortunately, noise emission model imposed by the END establishessound power level for two vehicles categories which don’t correspond to cal-culated vehicles1. Therefore, we need to elaborate data before being insertedin sound emission model: disaggregation of results in categories requestedby French sound emission model NMPB (official method, see D.L. 19/8/05)is performed together with sound levels measurements related to traffic flowsones to verify the reliability of disaggregation itself.The next step has been the creation of an IMMI project including necessarycartography to set up a digital terrain model. Moreover, 3D model has been

1Causes of this mismatch are fully explained in following chapters.

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CHAPTER 2. OBJECTIVES 4

prepared through 3D views and eye inspections: we used georeferred photosfrom GoogleEarth imported by the software and 3D visualization on LiveSearch Map website. Then sound levels calculation has been done with a5 m step grid through which we assign sound facade levels to residentialbuildings with Italian and European indicators.After that, results have been evaluated performing

• comparison between available measurements and calculated levels toestimate accuracy;

• evaluation of theoretical accuracy from GPG and other considerationsnot included in that paper;

• comparison between theoretical and real accuracy.

Moreover, thanks to population data of 2001 census, it has been possible tocalculate people exposure to road noise levels and the global exposure fromthe analysis of strategic noise map.

Before going through each step of this project, we want to explore the stateof art of noise mapping and international documents that must be takeninto account approaching noise mapping according the END:

• The Guide du Bruit and the French method NMPB indicated in theEND as the official method for road traffic noise;

• The IMAGINE project (Improved Methods for the Assessment ofthe Generic Impact of Noise in the Environment, see www.imagine-project.org): this project analysed different techniques of noise map-ping, included the ones with traffic model, emphasizing general diffi-culties;

• The Good Practice Guide ;

• European mapping experiences;

• Road noise map in the city of Florence published in February 2008 byARPAT and correlated studies on sound emission levels of differentcategories of vehicles;

• Previous noise map of Pisa;

• ARPAT study on sound emission of two wheelers vehicles carried outduring evaluation of acoustic clime in Pisa.

Chapter 3

The NMPB method for roadtraffic noise

3.1 Introduction

The calculation model for road traffic noise definitively adopted by the ENDis the French official method NMPB-Routes-96 (SETRA-CERTU-LCPC-CSTB), cited in �Arrete du 5 mai 1995 relatif au bruit des infrastructuresroutieres, Journal Officiel du 10 mai 1995, article 6� and in French law �XPS31-133� and successively adjusted to European indicators in commissionrecommendation 2003/613/EC. The NMPB (Nouvelle Methode de Previsiondu Bruit, [7]) takes into account meteorological effects on long distancepropagation: so it’s useful for modelling big infrastructures (as the ones tobe mapped according the END) in free field propagation conditions (its limitof application is 800 m far from the source). However, the critical problemof this model is the sound emission database: in fact, considered vehiclescategories are not always suitable to all European countries (two wheelersare not considered) and it’s not at all updated because the database is thesame of the Guide du Bruit of 1980 [8]. In next section the sound emissiondatabase is presented.

3.2 Guide du Bruit: sound emission DB for lightand heavy vehicles

Vehicles are treated as a point isotropic source 80 cm height above roadline: point approximation is good for almost all situations but we have topay attention when barriers are installed near the source (in this case ad hoccalculation method has been set up); the isotropic approximation is brokenonly 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

ρ0c2πr2

in which ρ0 is air density, c is sound speed and r is the distance from thesource. 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-spherebut we can calculate the sound power level for units length considering thenumber of vehicles per hour Q and the averaged speed v expressed in km/h:

(LW )m = LW + 10 log(

Q

1000v

)in which LW is the sound power level associated to a point source whoseequivalent length will be fully described in section 3.4.Database values are emission levels, that is the sound pressure level of asingle passage in an hour measured 30 m far from the source and at 10 mabove road surface.Sound pressure must be integrated in the referred time interval (i.e. anhour): [

p2]t2t1

=1T

∫ t2

t1

Wρoc

2πr2dt

This relationship can be rewritten considering a point source moving atspeed v and at a distance d under an angle of view θ:[

p2]t2t1

=1T

Wρoc

2πθ

d · vSo we can write:

[Leq]t2t1

= 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 theequivalent 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 logQ

Therefore, if we take into account the reference distance (and light air ab-sorption) and speed in km/h, the sound emission level is defined by thisequation:

E = LW − 10 log v − 50 = (LW )m − 20


These emission values are given by the Guide du Bruite for two vehiclesclass, for different type of circulation and type of slope of the road. Vehiclesclasses 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 al-though 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 thefollowing table we show values for some typical speeds and for all types ofcirculation in an urban context (emission values are given for an horizontalroad).

Table 3.1: Emission Levels in urban contextCirculation speed E light vehicles E heavy vehicles

Type [km/h] [dBA] [dBA]Fluid Continuous 30-50 29.5 44.0

50-70 30.5 42.560-80 32.0 43.0

Pulsed continuous 30-60 31.5 43.5Pulsed accelerating low 37.0 47.0

high 33.0 43.0Pulsed decelerating 29.0 36.0 - 38.0

Finally, we define emission of Ql light vehicles (EQl) and of Qp heavy ones(EQp): {

EQl = El + 10 logQlEQp = Ep + 10 logQp


Figure 3.1: Noise emission database [8]


3.3 NMPB-Routes-96: meteorological correction

In previous sections we explained that innovation of this method is howmeteorological effects are treated. Acoustically speaking, meteorologicalconditions are divided in three classes:

1. homogeneous conditions: sound energy propagates along straight lines;

2. conditions favourable to sound propagation: sound energy get downtoward ground giving much noise at receivers;

3. conditions unfavourable to sound propagation: sound energy rise to-ward the sky giving less noise at receivers.

NMPB considers only first and second conditions, not only because the thirdis difficult to calculate, but also because this assumption overestimates levelsand so it’s safer.The origin of meteorological effects on propagation is due to combinationof thermal gradient with aerodynamic factors of wind directions; in homo-geneous conditions these factors balance their selves, instead in favourableones acoustic rays go downwards.In fact, favourable condition occurs when wind direction is the same aspropagation one and when thermal gradient is positive (hot air is up) thatmeans 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 condi-tion, obtaining LF for favourable conditions and LH for homogeneous ones;after that, long term level is estimated by:

LLT = 10 log[p10

LF10 + (1− p)10

LH10

]in which p is favourable conditions probability.The NMPB [7] provides this probability for some French cities and suggests


to use 50% for day period and 100% for night period for all places where me-teorological databases are not available; that values of probability descendsfrom 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


3.4 Source characterization

Road source is represented by many point sources located along the centreline. Sectioning a road infrastructure in point sources needs to identifyacoustic homogeneous arcs: that means to define arcs as road sections withthe same geometrical profiles and with constant traffic flows. Each arc isthen split up into point sources to assign sound power. This splitting upmay be done in different ways:

• Equiangular splitting up1: the site is scanned from the consideredreceiver point by a group of rays whose angle step is constant (themore a receiver is close to source the more the step is small) and ateach intersection of one of these rays with a source line, a point sourceis placed;

• Splitting up with a constant step: each source line is split up intopoint sources regularly spaced out (the step between two consecutivesources does not have to be greater than half the orthogonal distancebetween the lane and the closest receiver point and the value of thestep shouldn’t be greater than 20 m)

• Variable splitting up: as the first method but with local variation ofangular step;

Finally, the sound power level for octave band j of each point source is givenby:

LAWi = 10 log(

10EQl10 + 10

EQp10

)+ 20 + 10 log li +R(j)

in which li is length (in meters) of road section represented by the currentpoint 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 inthe next table:

1This method is the once implemented by IMMI.


Table 3.2: Road noise Spectrumj frequency [Hz] R(j) [dBA]1 125 -142 250 -103 500 -74 1000 -45 2000 -76 4000 -12

3.5 Attenuations due to propagation

Sound power level of a point source has to be expressed as sound pressurelevel: to do this we have to take into account all attenuations of propagation.So, we have to calculate a different level for homogeneous and favourableconditions: {

Li,F = LAWi − (Adiv +Aatm +As,F +Adif,F )Li,H = LAWi − (Adiv +Aatm +As,H +Adif,H)

Values of each source are then long term evaluated and added above allsound trajectories and octave bands.We notice that geometrical attenuation Adiv

2 and atmospheric absorptiondo not change with propagation conditions3 and are expressed by:{

Adiv = 20 log d+ 11Aatm = α(j)d/1000

in which d is distance in a direct line between source and receiver, α is airabsorption coefficient expressed in dB/km:

j frequency [Hz] α(j) [dB/km]1 125 0.382 250 1.133 500 2.364 1000 4.085 2000 8.756 4000 26.4

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

2It’s divergence attenuation of an isotropic source.3They do not change with sound ray direction but the atmospheric absorption coeffi-

cient is given for a specific temperature (15◦C) and for humidity of 70%.


need to distinguish three zones of propagation: close to the source, inter-mediate, close to the receiver. Attenuation will be calculated as a sum ofthree contributions considering that in central zone rays are less influencedby surface attenuation. In homogeneous conditions rays are straight so wedon’t need to section zones. Formulas could be found in [7], however Asdepends on ground surface absorption G.Attenuation due to diffraction is caused by the lengthening of trajectorycompared with direct line one. Moreover, diffracted trajectory is influencedeven by ground absorption: in this case we have to separate calculation be-cause a diffracted trajectory in homogeneous conditions could be a directtrajectory in favourable conditions. In fact, ray curvature could make visiblea source and a receiver which couldn’t be in a straight trajectory (see figure3.5). Therefore, calculation of diffraction for favourable conditions consid-ers 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 modelsimplementation

4.1 IMAGINE project

IMAGINE project (Improved Methods for the Assessment of the GenericImpact of Noise in the Environment) was developed between 2003 and 2007:it was born as a scientific instrument of environmental policies support toMember States as a natural prosecution of HARMONOISE project. In thisprevious project harmonized emission models were developed for the entireEurope regarding railway and road noise pollution. IMAGINE’s aim was toproduce the same methods to estimate airport and industrial noise requestedby the END. Therefore, the common aim of both projects was to produceharmonized 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 indifferent 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 cir-culation management and mapping for action plans (Work Package 2);

3. produce guidelines and examples of how and when measurements areuseful 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 onHARMONOISE methods and guidelines for vehicles not in standardclassification (Work Package 5);

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CHAPTER 4. TRAFFIC AND NOISE MODELS IMPLEMENTATION15

6. produce noise emission database for passenger and freight trains basedon HARMONOISE methods and guidelines for vehicles not in standardclassification (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 alterna-tive to the ones suggested by the END: in fact, END methods are nationalmodels that are not always suitable to all European countries, anyway theyrepresent the standard by law. So, IMAGINE methods are nowadays notimplementable but the analysis carried out by working groups is still useful,even for this work with particular regard to comparison between mappingmethods.The work package WP2 is very interesting because of having reviewed allkind 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 mod-elling 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 thesame vehicle class), accelerations and fleet composition (number of heavyvehicles). However, these parameters haven’t the same effect on sound lev-els: if a doubling of flow is needed to raise levels of 3 dB, it’s sufficient avariation 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 toobtain a fixed uncertainty; the following significance order is given (i.e. theorder 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 im-prove speed accuracy: in fact, speed is usually taken equally to speed limits(not including congestions).Furthermore, IMAGINE WP2 carried out a Montecarlo simulation to evalu-ate which precision is necessary on speed and flow to achieve 0.5 dB or 1 dB


uncertainty on noise levels1. In figure 4.2 simulation results are listed.Apart from accuracy of input data, there are different traffic models thatare suitable to different situations and that differ on data requested and pro-vided; we exclude demand models (based on survey about travellers charac-teristics and choices) and we concentrate on different network models whichprovide speed and flow on each link of transport network. These kinds ofmodels are so classified:

• static assignment models;

• dynamic assignment models;

• continuous models;

• microsimulations models.

In document [13], produced by WP2, methods have been described throughSWOT analysis (Strengths, Weaknesses, Opportunities, Threats) to under-stand which one fits better data available (or deliverable), aim or time ofthe study. In the following sections, models characteristics will be brieflypresented.

Static traffic assignment

Static traffic assignment is the process of allocating trips in one or more tripmatrices (origin-destination matrices or OD-matrices) to their routes (paths)in the network, resulting in flows on links. After calculation, it’s possible toobtain reasonable link flows and speed and also to identify congested links.The basic assumption is that travellers choose the route that minimize theirtravel cost (cost and time are directly correlated); however, this methodis rarely able to distinguish vehicles classes, so after the assignment it’snecessary a class distribution on roads to produce vehicles categories.Moreover, static assignment is generally hour based: the origin-destinationmatrix contains for instance the trips of a peak hour. Capacities of theroad network are expressed as number of vehicles per hour. As a result, theestimated flows are averaged per hour and so we need a daily distributionto estimate noise indicators.

1Method used to produce noise from vehicles flows is HARMONOISE’s one, [12].


Table 4.1: SWOT analysis for static assignmentStrengths WeaknessesFast; All results are averages;Data collection relatively easy; Inaccuracy of resultsEasy-to-understand indicators; (especially speeds per link);Concise results; Usually night periods aren’tResults easy to use in GIS; modelled;Opportunities ThreatsVery common in local, regional Improvement of output forauthorities; noise calculations requiresMore digitized or automatically a large effort;generated data will become available; Model results can cause aRelative simplicity of models ensures false sense of accuracy: resultsthat new developments will usually seem detailed, but notbe tried out in static models first; all indicators are significant;

Dynamic traffic assignment

Dynamic traffic assignment is the process of allocating trips in function oftime in order to achieve time distribution of flows on network. Put intopractice, it means that instead of a single OD-matrix we have one for eachtime interval (an hour or a quarter) that we want to analyse. Many modelsimplementing dynamic assignment request that the temporal step of analysisis smaller than the shortest travel time over all network links (this meanslong run time in urban context).This assignment produces flow and speed as function of time and it makesalso possible to distinguish between different vehicles classes.

Table 4.2: SWOT analysis for dynamic assignmentStrengths WeaknessesCorrect modelling of demand Long run time;fluctuations; Few dynamic matrices forPossibility to model more accurately full day available;and correctly effects like congestion; Inaccuracy of resultsInclude multiple vehicle types; Times step size determinesResults easy to use in GIS; accuracy of results;Opportunities ThreatsDTA models offer possibilities in high It needs detailed input data;demand in impact assessment studies;More digitised or automatically Difficult modelling technique;generated will become available;


Continuous models

Continuous models treat vehicles as a unique entity idealized as a continuousfluid. 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 asshown in figure 4.1.This model is based on mathematical equations (solved sometimes numeri-cally) to produce flow, speed and density through time (time step from 0.5 sto 10 s). This type of analysis has an high level of detail and it’s commonin circulation management and not in noise studies: in fact, this model issuitable 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 ve-hicles: within each time step it uses a number of individual algorithms togenerate decisions for all vehicles on the network. Position and speed ofvehicles are updated at each step. There are two kinds of microsimulationmodels, the ones which consider a continuous network and the others thatconsider it as discrete (in this case the road could be occupied only if anothervehicle is on the road).These models require more input data about network itself as signalized in-tersection data, numbers of lanes, width etc., so they are suitable to littlelocal studies.

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


models often calculate single vehicles class so we have to pay attention tofleet composition; models are not reliable on low flow roads so we mustdecide how to treat them. These difficulties are greatest problems linkingtraffic model to noise model: that’s why WP2, in analogy with EuropeanCommission paper GPG, has written up a lot of toolkits to know previewedlevel of accuracy2 associated with a modelling choice.These toolkits will be used together with table in figure 4.2 to estimateuncertainty due to traffic model on noise levels calculated in Pisa.

Table 4.3: Models & OutputStatic Dynamic Continuous Micro simulators

Traffic Flows + ++ - +/-Speed + + ++ ++

Speed Distribution - + + ++Acceleration - - + ++

Fleet composition +/- +/- +/- +

++ available and reliable+ available- not available

Table 4.4: Models & Contest and reference TimeStatic Dynamic Continuous Micro simulators

Study AreaRegional/National ++ + N N

City + ++ N +Local motorways N + ++ +

Local urban N 0 0 ++Study periodPeak Hour ++ + + +

Day + ++ 0 +Year 0 0 N N

++ available and reliable+ available0 NeutralN not available/not common practice

2In this case is not a decibel accuracy but a quality evaluation.


Figure 4.2: Montecarlo simulation results - boundary regions of accuracy onflow 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 groupAssessment of Exposure to Noise (WG-AEN) in a first draft in 2003 andits final version was published in August 2007 after a long internationalconsultation process. The purpose of this Position Paper is to help MemberStates and their competent authorities to undertake noise mapping and toproduce the associated data required by the END3.This Position Paper dials with all possible problems applying the END andit tackles them with toolkits which suggest solutions and estimate associatedaccuracy.

3It’s especially oriented to the first round of sources to be mapped.


4.2.1 Accuracy evaluation: toolkits

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

Figure 4.3: Toolkits Legend

GPG suggests single steps accuracy but accuracy of final result is anywaytricky. In fact, uncertainty of each solution have to be combined to obtaintotal uncertainty but it’s not always known how them are related: theycould add or subtract each others, besides it’s common practice to admitdata belonging to independent Gaussian distributions and to do a squaresum. However, this practice is not indicated in GPG which purpose is onlyto give a scale of goodness for each technique. We will calculate square sumbut we will also carry on a comparison between measurements and calcu-lated levels to determine discards distribution.GPG toolkits are shown in figure 4.4: each one is split in different toolsaccording to available data. These tools will be taken into account for un-certainty calculation of Pisa road noise map.


Figure 4.4: Good Practice Guide step by step


4.3 European noise mapping experiences

4.3.1 Noise mapping of Pamplona agglomeration

Pamplona agglomeration includes 20 municipalities and it’s part of firstround noise mapping because it takes up an area of 127 km2 with a to-tal population of 280200 inhabitants. According to the requirements of theEND, strategic map has been performed including industrial sources, air-craft, railway and a total of 7441 roads [14].The area was very large so lots of iso-lines, curve lines and about 40000elevation points were used to build Digital Terrain Model.Calculation was performed with software Cadna/A with a 10 m step grid (asmaller step was impossible because of width of the area). Other technicalsettings 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 in-cluded both average speed and traffic composition. For the rest of notconsidered roads several criteria were implemented. One of them was toextrapolate from measurements to other similar roads. Another criterionwas to evaluate the traffic density as function of the density of populationto which the road is serving.In some cases great deviations were found but a pragmatic decision wasfinally taken and acceptable correlations with measurements were found.Average values of Hourly Average Intensity (HAI) expressed as vehicles perhour (v/h) for the six types of roads were as follows:

Table 4.5: Pamplona road classificationType Name HAI(day)

1 Outlying roads 10 v/h2 Quiet Residential Areas 50 v/h3 Residential 100 v/h4 Residential-Commercial 200 v/h5 Commercial 350 v/h6 Industrial 200 v/h

The hourly distribution of traffic for day, evening and night periods wereobtained from data continuously recorded at 66 measurement stations. The


percentages of heavy vehicles for different types of roads were obtained fromreal measurements in several roads and subsequent extrapolation to similartypes of roads. Several noise measurements were carried out with the aimto configure the calculation parameters according to NMPB model, mainlyto adjust the type of asphalts. Correlation between predicted and measuredvalues was quite high (differences were less than 1 dB for receiver pointsnear to emission lines). Nevertheless, differences increased with distance.Results showed percentage of people exposed to high levels: on day period9% and 37% population are exposed to levels bands of 55-65 dB and 50-60 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 agglomera-tions and major roads: these maps cover a very large area (only Glasgowagglomeration area is 766 km2) and include roads with more than 1000 ve-hicles daily passages. A specific traffic model, TMfS (Transport Model forScotland) was implemented to perform calculation [15]: TMfS is a multimodal traffic model which provides peak hour flows for three periods (AM,intermediate, PM) with heavy goods vehicles percentage. Instead, streetswith low flows inside agglomerations have been considered apart, assigninga typical flow. They implemented an automatic procedure based on arcsand 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 cor-rected by local authorities.Digital terrain model was calculated by steps [16], considering both twodimensional cartography and LIDAR information about height: cartogra-phy resolution was 5 m (i.e. based on 1:10.000 cartography); instead heightinformation was given rounded to meter. Moreover, they assigned a fixedminimum height of 5 m for all buildings.Regarding ground absorption they used information about rural areas todefine hard ground.Scottish government (and whole United Kingdom) made a strong effort toadapt 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 studyabout noise mapping in two different areas [17]: Carregado zone (Alenquermunicipality and suburbs) and urban area Linda-a-Pastora in the munici-pality of Oeiras. Noise estimation was performed with software MITHRA


using NMPB method: meteorological conditions were deduced from localdata, so probability of favourable conditions was taken as 30%.Different input/output resolution was chosen for each scenario: municipal-ity scale scenario was reproduced with 1:10000 cartography and noise gridstep was set to 18 m; instead urban scenario used 1:2000 cartography andan 8 m grid step. Moreover, municipality scenario considered second orderreflections and urban one considers third order reflections.All road sources were considered with an action radius of 2 km and trafficdata (flow and heavy vehicles percentage) were produced with automaticcounters (from local highway administrator), manual counts and by com-paratives analysis. Speed was estimated over all roads.A validation of noise maps was performed: average absolute differences be-tween measured levels and estimated ones was 1.5 dB (with maximum de-viation of 2.9 dB) over municipality scenario and 1 dB (with maximumdeviation 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 about1650 inhabitants that are not very annoyed (27% are affected by diurnallevels higher than 60 dB and nocturnal ones higher than 55 dB).Nevertheless, this project shows Portugal willingness of improving environ-mental noise policies which didn’t exist before European EnvironmentalNoise Directive (see [18]).

4.4 Tuscany case studies

4.4.1 Florence road noise map

Published in February 2008, noise mapping of Florence municipality followedan agreement between municipality, province and ARPAT regarding noisefacade calculation to provide PCRA. This study was carried out by U.O.IMREC of Florence ARPAT department and financed by municipality andprovince.

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 Trans-port buses flow was assigned to links according data given by local transportadministrator 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);


• counts campaign carried on by ARPAT on 25 streets (time period48h);

• 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 toItalian fleet; therefore, it was necessary to transform real vehicles in lightand heavy ones of NMPB database. Put into practice, this leads to the useof 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 heightCars (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/hspeed) and a mathematical relationship was defined to obtain equivalentflows: {

Ql,eq = nC ∗ 0.61 + nTW ∗ 0.87Qp,eq = nB ∗ 0.29 + nHGV ∗ 0.12


Calculation reliability

Before treating reliability, we want to underline calculation settings: trafficsource was considered as a source on centre line with standard asphalt androad gradient was supposed null for all roads. Moreover, calculation tookinto account first order reflection and 10 m step grid is used. Facade levelswere 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 pointswere yet available from older campaigns (1995-2003). Results showed thatonly 22 measured points presented a difference from calculated values lessthan 3 dB. Main reasons of these errors are problems on 3D model realiza-tion, measurements reliability (too old to represent current situation) andmismatches between assigned traffic category and real flow on the road. Thislast problem results greater on night levels so that probability of residualsover 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 withPisa municipality for the PCRA. This action plan shall identify buildingswhere facade levels are higher than the ones established in the acoustic clas-sification plan (PCCA) to manage reduction measures according to priorityindex defined by DM Ambiente 29/11/2002 in annex 1.In fact, maps simplify the identification of hot spots (i.e. critical areas) andthe comparison with limits established by law. In September 2006 day lev-els map was produced[6] and at the beginning of 2007 the night levels mapfollowed[22].Moreover, exposed population was evaluated according to END exposurebands and European indicators (population from 1991 census).

Source characterization

Traffic flow on local network was assigned identifying homogeneous classesof streets and then a standard flow was assigned. Standard flow was basedon 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.4Measurements devices without direct control


Roads classification was based on suggested GPG classification (see tools 2.5,4.5) and adapted taking into account PUT indications; to estimate night lev-els, a different coefficient per class was applied. Speed values were estimatedassigning 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 Mira-coli) and classes 40, 30-32 it’s immediately given by intersecting roads andpolygonal shape of ZTL and 30 km/h zones; instead other classes needed acomparison with PUT maps. Roads classification is shown in figure 4.7.

Figure 4.7: Roads classifications [6]


As like in Florence, this work used an acoustic weighting to obtain NMPBequivalent light and heavy flow: same weight of ARPAT study [20] was usedregarding 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 weightedon other roads.

Propagation and calculation settings

Sound levels calculation was provided with software IMMI version 5.2 thatimplements NMPB method. All streets were inserted in the IMMI projectwith a good asphalt; streets were set as double direction (the one-way onestoo) and total flow was placed on centre line.Terrain model was developed through regional cartography CTR 1:10000using altitude points, iso-lines and curve lines, bridge altitude lines and hy-drological 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 clearlymistaken (buildings minimum height was set to 3 m).To manage calculation, simplified method was used which takes into accountreflections from surfaces: buildings were considered completely reflective in-stead of ground whose absorption was set to 0.5 (according to residentialareas absorption suggested by GPG in tool 13.1).Meteorological correction considered only favourable condition (p = 1) ac-cording ISO 9613-2 and temperature of 25◦ C and humidity of 50% wereset.Calculation was done dividing municipality in 200-300 m large zones andperforming a grid with a step of 10 m.

Uncertainty evaluation

An accurate analysis was performed through GPG toolkits and other doc-uments ([20] and [24]). Therefore, accuracy was evaluated for each step ofnoise mapping process and table in figure 4.9 summarizes theoretical accu-racy 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 aglobal theoretical uncertainty of 4.3 dB and night levels of 4.6 dB.These uncertainty values were validated through a comparison with mea-surements: differences were calculated between modelled values and contin-uous (full day without direct control) and spot (an hour with direct control)measurements.Measurements were done by ARPAT to define Acoustic Clime in 2005-2006or more generally to define road noise: day levels comparison was done on54 continuous positions and 106 spot ones; night comparison was done on51 continuous and 94 spot. Notice that night levels of spot measurements


were estimated according ARPAT guidelines [25] based on flow distributionper road class. Table from [25] follows, reporting correction factors for nightlevels and suggested measurement time interval for each road class:

Road Type Measure Days Time period CorrectionUrban or local road with Mon.-Sat. 9.00 - 11.00 8 dBlow flow and low %HGVInter-district or suburban Mon.-Sat. 10.00 - 12.00 6 dB

road with low %HGVMain suburban roads Thu.-Fr. 12.00 - 15.00 5 dB

and highways

Comparison evidenced that:

• 90% day levels residuals were between 4.6 dB and -4.8 dB with median0.11 dB;

• 80% night levels residuals were between 5.3 dB and -4.1 dB with me-dian 0.78 dB.

By the end, we could say that residuals were distributed as previewed byGPG, i.e. a better result couldn’t be possible with available input data.Differences between calculated values and measured ones are shown in figure4.8.

Figure 4.8: Residuals Distributions


Final considerations

Improvement of accuracy is possible only with an improvement on trafficinput data: already conclusions of first noise mapping report presented thepossibility of a traffic model (recently bought by ARPAT) to estimate noiselevels 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 recommendedafter the first map.Finally, conclusions of that report emphasized that mapping is a dynamicinstrument to be updated and verified: in fact, only if it’s updated, it couldbe a strategic help to protecting policies. So ARPAT continued working inthis direction: this paper updates acoustic map with a traffic model and it’spart of the larger project of producing strategic map (including railway andaircraft noise).


Figure 4.9: Day model steps and accuracy [6]


4.4.3 Two wheelers sound emission evaluation

This study is part of a three-years master thesis of 2004 [27]: it’s a com-plementary study of the evaluation of acoustic clime in Pisa committed bymunicipality to ARPAT.The aim was to evaluate two wheelers contribution to environmental noise insome city streets: particular attention was given to ZTL streets of historicalcentre (here two wheelers circulate free in spite of cars). Moreover, it triedto determine average spectrum of two wheelers (divided between 50 cm3 andthe ones higher powered).Sound levels were measured with spectrum analysers: levels were post-processed to quantify two wheelers contribution. Single passages were iden-tified within time history and total contribution was evaluated per type ofstreet.After this analysis critical streets were evidenced to manage PCRA whoseaim is an efficient noise reduction.

Achievements

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


Figure 4.10: Measured SEL from [27]

Chapter 5

A new approach to trafficassessment

5.1 Introduction

This work innovation consists in traffic assignment method: traffic modelis implemented with software TransCAD version 4.8 (powered by CaliperCorporation and licensed to ARPAT). Utilization of this software startedin 2006 to support data elaborations to be done for PCRA agreement withPisa municipality; unfortunately, no traffic network analysis was carried outbecause there wasn’t enough time within dead line of PCRA. Traffic projectrestarts in September 2007 with this thesis: the first job has been to collectall data of first attempt, then to understand, update and enlarge data tomirror 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 highmobility routes (LAM verde, rossa, blu).

Moreover, viability next to railway station is actually influenced by con-struction of underground parking area. So, we have to decide how to modelcirculation 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 estimatefuture 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 longterm noise and mustn’t mirror old or temporary situations.During predisposition of traffic network, we tried to create a project as up-dated as possible providing necessary data and trying to obtain them fromauthorities: we collected counts of ZTL gates from PisaMo (mobility agencyof Pisa) and we requested lights cycles to municipality which didn’t thoughtconvenient to furnish them1.

5.2 TransCAD characteristics

TransCAD puts together GIS capabilities and traffic flow management pos-sibilities: it is useful both to traffic managers of administrations and to farmswhich want to know citizens routes.Any model producing flow on roads must start from a network and an OriginDestination matrix to know how many travellers go from origin Oi to des-tination Dj , that means to calculate flow path vector p or flow links vectorf . The software provides many methods to evaluate matrix based on citizencharacteristics (number of cars per family, income, population density. . . ):the matrix created during the first attempt was probably calculated in asimilar way, but it considered too large OD areas making impossible to esti-mate traffic inside them. We decided to define a new matrix with more ODpairs.However, there were no data available about attraction or production of flowper area, so we used another estimation method. In fact, TransCAD is ableto 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 utilizedassignment.

Put into practice, this method creates a matrix according to counts andthen it assigns flows to all other links through the selected method.Assignments implemented in TransCAD are all static assignments (the onessuitable to strategic purposes):

• All or Nothing : all traffic demand is assigned to shortest path withoutcapacity restraints;

• STOCH assignment : calculation of path choice probability is per-formed by a proportion between travel times;

1New lights cycles management was going to be furnished to municipality by a privateagency but it wasn’t tested yet.


• Incremental assignment : at each step only a fraction of traffic demandis assigned through All or Nothing assignment, then travel times areupdated as function of flow already assigned;

• Capacity restraint : as previous one, but travel times are updated tak-ing into account maximum capacity on links (this method may notconverge to equilibrium);

• User Equilibrium (UE): it use an iterative algorithm that reaches equi-librium 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 travellersdon’t know exactly network conditions and they have different per-ceptions of travel times;

• System Optimum Assignment (SO): as UE, but it reaches equilibriumwhen total travel time is minimized (it’s not a realistic condition butminimize congestions).

We selected UE method because is quite realistic and converges in a limitednumber of iterations: this method has a precise mathematical definitionand we can demonstrate that utilized algorithm converges. This algorithmis Frank-Wolfe iterative process. In fact, at municipality scale, we needa model which takes into account congestions (i.e. capacity) and whichconverges to equilibrium solution; otherwise we could have long run timewithout reaching a correct solution.

5.3 User Equilibrium Method

Let’s precise the User Equilibrium concept: UE is usually called also DUEbecause it assigns a deterministic utility, i.e. cost, to links. Equilibriumconditions are expressed by the Wardrop’s First Principle presented in 1952:

Theorem 1 (Wardrop’s First Principle) IF traffic demand is constanton project time, capacity restraints are not active and travellers behaviour isdeterministic at full knowledge, THEN at equilibrium point, all paths betweenan OD pair with not zero flows have all the same travel cost, instead all theothers have equal or higher costs.

That is:p∗k > 0⇒ C∗k ≤ C∗h ∀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 itfollows next equation, called variational inequality:

C(p∗)T · (p− p∗) ≥ 0 ∀p ∈ Sp


DemonstrationIn fact, if p∗ is an equilibrium vector only minimum cost paths are usedand any other distribution p should use other paths with equal or highercosts so equation follows. Vice versa if p∗ verifies the equation it verifiesalso Wardrop equilibrium condition: in fact, if it would exist a positive flowon p which is not minimal, then we could obtain a vector p which wouldn’trespect Wardrop condition. For example, let’s consider to translate minimalflow 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 dis-tribution pt, and relative costs vector C(pt). At next time t + 1 flow pathvector will change only if exists a path with a lower cost than the actualone. So flow vector evolves to equilibrium:

C(pt)T · (pt+1 − pt) < 0

Wardrop equilibrium condition may also be expressed as links (from nodei to j) flow fij , instead of using flow path vector, through the followingsubstitutions:

fij =∑

k∈OD pkδij,k

δij,k ={

1 if ij ∈ k0 if ij /∈ k

=⇒ C(f∗)T · (f − f∗) ≥ 0 ∀f ∈ Sf

Vectorf∗ is called links flow equilibrium vector and it exists and it’s uniqueunder following conditions.

Theorem 2 (Existence) If C(f) for all links is continuous then equilib-rium vector exists.

DemonstrationTo demonstrate this theorem, we have to introduce T (f) = f−C(f) definedin Sf . Let’s consider a generic f ′ and the vector f ′′ ∈ Sf which minimizesdistance to T (f ′):

f′′

= min{H(f, f ′) =[f − T (f ′)

]T [f − T (f ′)

]}

in which H(f, f ′) is a scalar function only of f . Hessian of this function isa positive definite matrix (double of identity matrix), so H(f, f ′) is strictlyconvex.


Since Sf is closed, then it’s sure that f ′′ exists and it solves minimum prob-lem and it’s also virtual minimum for H(f, f ′), i.e it follows next equation:

∇H(f ′′, f ′)T (f − f ′′) ≥ ∀f ∈ Sf

Moreover, thanks to strictly convexity minimum point is unique. Now let’sconsider a function which associates f ′′ to f ′: this function is defined inSf and its image is still in Sf so it’s continuous only if it’s continuousC(f). Browers’s theorem asserts that a continuous function, with imagecontained in definition set, has a fixed point f∗: this point verify virtualminimum condition for H(f, f∗). This condition is true for all f thereforewe 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 mono-tonic strictly crescent, that is if:

[C(f1)− C(f2)]T (f1 − f2) > 0 ∀f1, f2 ∈ Sf

DemonstrationIf we would have two different equilibrium vectors, then we could considerone as f and the other as f∗ to write equilibrium condition or vice versa andwe would obtain a contradiction with hypothesis of monotonic function:

C(f∗2 )T (f∗2 − f∗1 ) = C(f∗1 )T (f∗2 − f∗1 ) + [C(f∗2 )− C(f∗1 )]T (f∗2 − f∗1 )

which takes to C(f∗2 )T (f∗1 − f∗2 ) < 0. This leads to contradiction of equilib-rium condition for f∗2 .

QED

The sufficient condition for cost functions monotonicity is that Jacobianmatrix J [C(f)] is positive defined over whole Sf : elements of this matrixare 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 situationJacobian 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


calculation should be performed.However, if Jacobian is symmetric, equilibrium vector is calculated as:

f∗ = minf∈Sf

∮ f

0C(x)dx

Moreover, if cost functions are dissociable we can express f∗ as:

f∗ = minf∈Sf

∑ij

∫ fij

0Cij(xij)dxij

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 problemminimizing the following quantity:

Z(f) =∑ij

∫ fij

0Cij(xij)dxij

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 fora descendant direction of Z(f) (for a convex function descendant directionmeans minimum one). If at step k a solution fk is given, it looks for adistribution fk+1 closer to minimum. To find this distribution it’s necessaryto expand linearly Z(f)2:

Z(f) = Z(fk) +∇Z(fk)T (f − fk)

Notice that a minimal distribution for Z(f) is minimal also for ∇Z(fk)T f ;because cost functions are dissociable, Jacobian is diagonal and so distribu-tion is minimal for C(fk)T f . So descendant direction is identified by flowauxiliary vector fk defined as minimum for C(fk)T f :

C(fk)T (fk − fk) < 0

Then calculation of fk+1 is performed looking for a point between fk andfk:

fk+1 = fk + λk(fk − fk)2Calculation is taken from [28], [29] and [30].


Put into practice, we look for λk Lagrange multiplier which minimizesZ(fk+1): fk minimizes total cost over the network so it’s the vector ob-tained 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 Nothingassignment with costs of zero flows;

2. we update costs;

3. we calculate fk performing All or Nothing with updated costs;

4. we calculate λk performing bisection method: partial derivative of Zwith respect to λ is performed in central point and we iterate on rightportion with positive derivative or left otherwise;

∂Z(fk+1)∂λ

=∑ij

Cijfk+1ij (fkij − f

kij)

5. we update flow vector fk+1 ;

6. we decide how to stop algorithm with a convergence test throughthreshold ε:

maxij

∣∣∣fk+1ij − fkij

∣∣∣fkij

< ε

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 acollection of links and nodes with quantitative attributes. Transport networkis the one whose quantitative attribute is cost function.We already said that Frank Wolfe algorithm converges to equilibrium ifwe use separable cost functions: this condition means that the functionreferring to link i is not influenced by flow on other links. Therefore, it’sa big approximation in urban context because we are asserting that traveltime on a street is not influenced by flows of intersecting roads.With this hypothesis, cost function used by TransCAD is a function thatprovides travel time t:

t = tf

[1 + α

(vc

)β]in which

• tf is free flow travel time calculated as link length divided by free speed(law limit speed);


• v is traffic volume assigned;

• c is link capacity (maximum number of vehicles which can transitduring 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 otherparameters (beauty of landscapes, monetary cost of travel etc.).To define OD matrix it’s necessary to distinguish OD nodes from intersec-tion ones: OD nodes are called centroids and they are traffic accumulationpoints. Therefore, traffic on centroids is not necessary balanced and theyidealize parking areas. Moreover, centroids aren’t directly linked to realnetwork but through connectors that do not correspond to any real street:these connectors are in place of local streets whose traffic is not possibleto be estimated. Local streets traffic depends on inhabitants distributionand cannot be calculated from the global network. Suburban, urban andinter-district streets have been inserted in the network excluding some localstreets; nodes have been divided into centroids and intersection nodes.In figure 5.1 is shown the network with centroids and connectors empha-sized.TransCAD allows inserting delays based on turn type (crossing, left or rightturns) as a function of links classification and delays on specific nodes. Thesedelays will be added to travel time t on path to make more reliable calcu-lations. Following sections describe road classes parameters and used inter-section delays.

Figure 5.1: Traffic Network: centroids and connectors


5.5.1 Data collected from first step of TransCAD utilization

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

Table 5.1: Traffic data availabledetection year time temporal n◦ vehicles n◦ and typetechnique interval detail categories of placesmanual 1999 7.30-10.30 15′ eight 17 boundary

roadsmanual 2000 7.15-9.30 15′ unique 30 junctions

laser detectors 2006 24h 1h five 10 streetslaser detectors 2007 24h 1h five 1 streetvideo cameras 2006 24h 1h four 7 gates

Obviously time interval and vehicles classes differences make immediatelydifficult to use these data: by the end, we decided to discard intersectionsdata because it was impossible to obtain vehicles categories. All other countshave been reviewed to verify possible invalidation due to circulation changes.An OD-matrix was also prepared probably based on PUT data (publishedin 2000) providing movements between areas; anyway we decided to ignoreit 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; roadswere classified according PUT classification and following parameters werealready 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 = 0.15and β = 4 to include the approximate effect of intersecting flows and in-tersection delays associated with a link. Therefore, these values have been


taken as correct.These parameters have been estimated using values suggested by HighwayCapacity Manual [31] for bidirectional roads: notice that HCM suggests val-ues for a not dissociable cost function where v is flow of both links ij andji. Values of HCM are:

Table 5.2: Parameters from HCMType of road α β

divided highway 0.1 13.75 m large per lane 1 2.53.00 m large per lane 3 4

Moreover, classes have been enlarged to include highways with link type 0and to assign different parameters within the same class according real con-text of roads. In fact, this first classification didn’t include 30 km/h zonesand all ZTL, so the new one is summarized in following table.

Table 5.3: Road link types and inputsRoad type Link type Capacity Free Speed α β

Highways 0 3600 90 0.1 1Inter-district 1 2600 50 2.5 4large roads 1300 50 2.5 4Suburban 3 1600 70 1.5 3

roads 1500 60 1.5 3Connectors 5 9999 30 1 1

1000 30 1 1District and 6 1000 40 3.5 4local roads 500 30 3.5 4

Inter-district 7 2600 50 3 4roads 1300 50 3 4

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


Figure 5.2: Traffic Network: road types

5.5.3 Intersection delays

Intersection delays have been inserted as turn penalties without includingconsiderations about congestion, that is a fixed delay has been assumed.This kind of delay depends on link type because we considered that, apartfrom connectors, links with higher capacity are major roads respect to oneswith a lower one. These delay values have been taken as the critical timegap needed in major road flow to do a specific movement from the minorone (left, right turn and crossing).Critical gaps are the ones described in High Capacity Manual [31] for stopsignal (see figure 5.3): in this manual critical gaps are used to define a delaybased on assigned flow and to perform an assignment with volume-dependentturning delays, so taking them as fixed produce an underestimation of delays.In fact, equation for volume dependent delay (seconds per vehicle) is thefollowing:

d =3600cp,x

+ 900T ·

vxcp,x− 1 +

√√√√( vxcp,x− 1)2

+

(3600cp,x

)(vxcp,x

)450T

+ 5

in which T is analysis time period and cp,x is potential capacity for movementx which is calculated as function of critical gap tc, follow up time tf (timeneeded to do single movement) and conflicting volume vc:

cp,x = vc,xexp(−vc,xtc/3600)

1− exp(−vc,xtf/3600)


Figure 5.3: Critical gap criteria for unsignalized intersections

TransCAD provides a tool performing assignment with volume depen-dent turning delays: despite having inserted all inputs requested, tool breakswithout assigning flows. This problem could not be solved, so fixed turnpenalties have been used for both unsignalized and signalized intersections.In addition to turning delays, specific penalties have been inserted at ZTLgates to simulate low flows.

5.6 OD matrix calculation

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

3An attempt to perform PM peak was done but calculations produced values similarto morning ones.


With explained conditions it’s possible to estimate matrix from traffic flowconsidering that base OD matrix elements di differ from real demand di:di = di + θi. Moreover, also flows are not perfectly assigned so if H is theOD matrix, d is demand, we obtain estimated flow as: f = Hd+ ε.To estimate demand matrix we can solve an optimum constraint problem us-ing Bayesian estimator d∗ which combine prior demand d with experimentalflow f :

d∗ = mind≥0

∑i

(di − di)2

var(θi)+∑j

(fj −∑

l hljdl)2

var(εj)

This problem can be solved using an iterative algorithm called steepest de-scent or gradient descent method:

1. we define an acceptance threshold ε and we initialize dk = d;

2. we calculate objective function D(dk) and not restraint direction:gk = −∇D(dk);

3. we calculate steep direction hk:{hki = gki if dki > 0 or gki ≥ 0

hki = 0 otherwise

4. we look for λk along hk direction:

λk = min0≤λ≤λ

D(dk + λhk)

in which λ is maximum values which ensure not negativity of dk+λhk;

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 throughthreshold ε: ∣∣D(dk+1)−D(dk)

∣∣D(dk)

< ε

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 countshave been used to estimate matrix. In figure 5.4 a map of sample links isshown. However, counts are useful also to estimate day and night trafficdistribution so many counts along the whole day have been done to validatethe model.


Figure 5.4: Traffic Network: sample counts

Counts are collected in different ways:

• manual counts: distinguishing cars, two-wheelers, heavy goods vehiclesand buses;

• video cameras: distinguishing cars, two-wheelers, light goods vehicles,heavy ones;

• laser traffic counter: with Viacount we could have speed distributionand counts distinguished by vehicles length (following section will de-scribe categories through length).

Total counts are summarized in the following table:

Table 5.4: Traffic Countsn◦ counts Period Time Method Notes

13 Sept 06/Sept 07 24h video cameras by PisaMo12 Oct 07/Feb 08 1h manual this project17 Nov 99 3h manual acoustic clime9 Jul 06 24h Viacount I PCRA project19 Oct 07/Feb 08 24h Viacount II this project


5.6.2 Equivalent vehicles

TransCAD provides also multi modal assignment: to perform multi modalassignment, an OD matrix for each category is needed and there’s a toolwhich allows estimating multiple matrices. So, we tried to estimate heavyvehicles, cars and two wheelers matrices but heavy vehicles and two wheelerscounts were too low to have e reliable assignment.Therefore, equivalent vehicles have to be used: as like as NMPB vehicleswe have to define a weight but instead of acoustic power it must relate onroad occupation. In fact, a road that reaches maximum capacity for x cars(nC) will reach it with fewer heavy goods vehicles (hereinafter HGV) andwith more two wheelers (nTW ); so we decided to define factors to converttwo wheelers and heavy in passenger cars equivalent flow as shown in thefollowing equation.

neq = nC + 0.5 · nTW + 2.5 · nHGV

Equivalent vehicles have been calculated from manual and cameras countstaking buses as HGV and light goods vehicles as cars.Counts performed with laser automatic traffic counters must be analysedwith more attention: counters identifies vehicle length from axis distance,more exactly a laser beam starts from counter and see different lengths ac-cording 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 eachlane (the one closer to counter has shorter boundary) according detectordistance from road less than 0.5 m:Notice that automatic detectors could underestimate or give unreliable countswhen closer lane traffic obscures opposite lane one. The resulting effect is anhigh number of short vehicles which corresponds to pieces of normal vehiclesnot completely detected: this means that opposite lane values are reliableonly if traffic is quite low.


Table 5.5: Automatic Counter categories from [32]category Length in closer lane Length in opposite lane

two wheelers < 322 cm < 400 cmcars 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 ODmatrix 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: publictransport buses have been considered as pre-loads and assigned to linkswhere they haven’t a reserved lane. Bus scheduling has been taken fromCPT website and LAZZI website; urban lines routes have been identified bymap on CPT website, instead suburban lines routes have been asked to busdrivers4. 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.4In spite of written requests no data were furnished by CPT.


Time and speed are the ones assigned at last iteration that is time corre-sponds 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 use-ful to identify hot spots through creation of thematic coloured maps: Tran-sCAD provides maps in which colours scale follows V/C ratio and thicknessof links follows volumes. Such a map, produced on last assignment, is shownin 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 factmany changes to network have been done before achieving a good balancebetween number of streets mapped and flows on them. In fact, if we considertoo many streets, model is not able to assign flows correctly and we see effectsof All or Nothing assignment: let’s consider two local streets going in thesame direction (e.g. Porta a Lucca, CEP, Pisanova areas) connecting to thesame major roads. In this scenario, model assigns all traffic to the shortestpath and nothing to the other because flow is low. Therefore, such streetsshould be represented by only one connector or have an high flow. Otherproblems occur when too few roads are in the projected network (respect tothe real one) and calculated flow increases extremely.


Finally, we obtain a result that gives flow quite correctly: in figure 5.7distribution of residuals is shown according uncertainty on noise levels fromIMAGINE 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 betterresult because only 73% of residuals differ less than 22% which was minimumaccuracy to obtain levels uncertainty of 1 dB according [11].We were unable to verify directly speed results because too few counts havebeen achieved with reliable speed: anyway we considered speed calculationcorrect for day period and we assumed speed limits for night period. Noticethat speed calculation is conditioned by limits: more the flow is higher, morethe speed is small but often free speed assumed is too low because manytravellers don’t respect limits (especially on longer links).

5.9 Uncertainty evaluation

Traffic flow has been assigned to network for peak hour and equivalent ve-hicles.We want to evaluate decisions according IMAGINE toolkits to give an esti-mation of accuracy; principal aspects are:

• Speed;

• Acceleration;

• Traffic composition;

• Diurnal and long-time patterns;

• Low flow roads;


• Intersections;

• Gradient.

Figure 5.8 summarizes decisions and shows associated accuracy from [11]:notice that higher number of polygons angles means higher accuracy andtoolkits provides triangle to hexagon degrees. If we assign an accuracy fac-tor 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 ac-curacy is quite poor. As already discussed, it would be unlikely to achieve1 dB uncertainty over all links.To evaluate numerical accuracy we will consider GPG source-related toolk-its (Toolkits 2-7). However, GPG toolkits include implementation of noisemodel so here only traffic model features will be analysed related to IMAG-INE toolkits.The use of a traffic model on major roads to estimate flow is considered byGPG (Tool 2.5) having an accuracy of 0.5 dB. However, we have to remem-ber that estimation on other roads will use first map flows so with 2 dBaccuracy technique.Tool 3.5 provides accuracy for speed data: speed from traffic model has beenused for day period on major urban roads; speed limits have been used fornight period and for all other low flow roads. Therefore, 1 dB is associated tomajor roads day levels, instead night and low flow roads levels are affectedby 2 dB uncertainty. Moreover, tool 6.1, regarding junctions, suggests a1 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 mod-elling measures not explained yet.


Figure 5.8: Traffic model decisions

Chapter 6

TransCAD traffic outputelaborations

6.1 Introduction

Traffic models are not born to perform noise calculation so many problemsoccur when output of traffic model is used as input of noise models. Principalreasons 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 mis-matched with the real one. This means that road profiles should be verifiedbefore 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 esti-mate LDEN and LNight, so we have to extrapolate daily distribution to haveaverage day evening and night traffic data (see section 6.3).Finally, traffic data don’t distinguish between vehicles categories, so we haveto apply distributions to obtain NMPB vehicles (see section 6.4).All these problems are solved with a level of accuracy which is treated atthe end of this chapter.

6.2 Spatial resolution

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

55

CHAPTER 6. TRANSCAD TRAFFIC OUTPUT ELABORATIONS 56

period.After having adapted periods, we substituted calculated roads to the oldones and we left the old flow on ignored links: this geographic merge is noteasy because links don’t match exactly so a manual control on each zonehas been done1. All connectors and centroids have been deleted; figure 6.1highlights 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 inthis area and we modified flow on them; a lower flow has been assigned alsoto dead end streets whose traffic was unrealistic.Spatial resolution problem due to the traffic model causes streets to lie tooclose or under buildings on noise model: all streets lines have been modi-fied to lie at the correct distance from buildings by means of GoogleEarthintegrated with IMMI software. This procedure regarded not only streetscontrol, but also building destination and other adjustments of the noisemodel, so it will be discussed later.

1This geographic passage has been performed by arch. C.Chiari of ARPAT departmentwith a sequence of automatic and manual operations on layers.


6.3 Temporal resolution

Before merging traffic data with the old ones, we needed to produce trafficflow representative for day, evening and night. Many counts were performedwith automatic detectors for an entire day: so, it was possible to derive flowdistribution on sample roads and apply it on similar roads. Then we esti-mated coefficients to be applied to peak flow to derive flow on day, eveningand night period. GPG suggested such values distinguishing between mainroads 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 beencalculated from measurements; a new classification of roads has been donebased on day-peak traffic ratio. In fact, flow time distribution doesn’t nec-essarily 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 pro-file, but more generally because percentages of day, evening, night flow re-spect to peak one are similar. Through these percentages flow periods co-efficients are established for each class and time period; if α is percentagelisted in the following table, average hourly flow for referenced period T isobtained as follows:

QT = αT ∗QPeakIn figure 6.3 examples of distribution of classes 2, 3 and 4 are shown; hourlyflows are divided by peak hour flow.


Table 6.1: Percentage of flow for different time periods in PisaRoad class Italian day Day Evening Night

6.00h-22.00h 6.00h-20.00h 20.00h-22.00h 22.00h-6.00h1 1 1 0.7 0.22 0.82 0.87 0.45 0.163 0.84 0.86 0.54 0.164 0.71 0.78 0.44 0.12

Figure 6.3: Examples of time distributions

6.4 Traffic data resolution

6.4.1 From equivalent vehicles to real vehicles

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

Road class Two Wheelers (TW) Cars (C) HGV∗

1 0% 80% 20%2 8% 89% 3%3 15% 82% 3%4 45% 52% 3%

∗ Bus not included


If we would have estimated total vehicles flow, then a simple ratio could beapplied to obtain real vehicles: instead we have equivalent flow so a regres-sion must be applied. In fact, if p is percentage, number of vehicles is givenby Q = p ∗ T where T is total flow but we have to express T as a functionof total equivalent flow E.

E = 0.5 ∗ pTWT + 1 ∗ pCT + 2.5 ∗ pHGV T=⇒ T = E ∗ (0.5 ∗ pTW + 1 ∗ pC + 2.5 ∗ pHGV )−1 ≡ E ∗ β

So flows per category are:QTW = pTWβE ≡ γTWEQC = pCβE ≡ γCEQH = pHGV βE ≡ γHGVE

γ values are listed in the next table for each road category.

Road class γTW γC γHGV1 0 0.62 0.152 0.08 0.89 0.033 0.15 0.85 0.034 0.56 0.63 0.04

Notice that we never consider buses: as presented in previous chapter busroutes and scheduling are known as hourly flow so it would be wrong toestimate them. Buses flows are included in traffic models as pre-loads so wecould, after the assignment, subtract them. Buses are considered as specialcategory whose distribution is completely known: no time period correctionis needed.Finally, we have an estimated flow per link at each reference period dividedin 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 NMPBemission values to real ones: we wanted to verify real emissions in Pisa tak-ing as reference values the Florence’s ones.Following this approach, four measurements have been done: sound levelshave been memorized together with time of single vehicles passages. Mea-surements were performed in different road classes, choosing streets withhigh public transport flow to evaluate their contribution. Of course singlepassages are very difficult to identify, however we identified 46 cars, 53 busesand 31 heavy vehicles passages. Instead, two-wheelers emission values havebeen calculated from ARPAT study described in section 4.4.3.For each single passage, SEL has been calculated from time history, thendivergence and time correction has been applied to obtain emission values


at 30 m distance and 10 m height (measurements were performed at 3 mdistance and 4 m height). Notice that emission values of NMPB model arecalculated in a free field condition but we were always near facades so wedecided to subtract 3 dB.Same thing has been done with values furnished by [27]: we must considerthat all categories have been averaged together, so about 250 two-wheelerspassages 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 33.4Pisa 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 andso the same weight has been assumed; differences between bus and heavyvehicles are very low (considering that measured SEL3 vary about 4%) sowe assumed the same weight (from higher values).Weights are here listed:{

Ql,eq = QC ∗ 0.61 +QTW ∗ 1.2Qp,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 thatPisa links are very short and acceleration and deceleration influence ishigher, anyway experience teaches that two wheelers are really noisierthan cars so a coefficient higher than 1 is correct;

• buses are noisier in Florence; this is probably due to Florence bus fleetthat 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 ofuncertainty:

• it considers week-end flow as weekday one and so 1 dB uncertainty isgiven in tool 2.3;

2According to average speed, cars and two-wheelers have been compared to 50 km/hvalues, instead buses and heavy vehicles to 40 km/h values.

3Explanation of this indicator is given in appendix A.


• it’s possible that road categories are too simple and don’t mirror realtime traffic distribution.

However, measured time coefficients vary within the same class less than25% so equivalent flow varies changing levels less than 1 dB.Distribution method to estimate traffic composition is the second option ofGPG 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 about20%: therefore uncertainty expressed in dB is less than 0.8 dB. Consider-ing 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 uncer-tainty due to composition of 0.5 dB.Weight accuracy was estimated in first map work [6]: 1 dB accuracy was sug-gested but no clear explanation was given. Therefore, a test was managedto verify this accuracy: test was done measuring sound pressure levels andflows at the same time and then verifying estimated values. Flow and soundlevels were averaged on a week and then light and heavy vehicles were calcu-lated using weights on measured categories; day, evening and night measuredflows have been inserted in the noise model and levels calculation on recep-tion point (i.e. measurement position) has been performed. Measured andcalculated values in Via di Gello (test location) are compared in followingtable:

Day Evening NightMeasured 65.4 dB(A) 65.7 dB(A) 58.7 dB(A)Calculated 66.6 dB(A) 64.6 dB(A) 56.6 dB(A)Residuals -1.2 dB 1.1 dB 2.1 dB

Differences increase during night period because speed change: in fact as-sumed speed was equal to all periods instead the real one increases. Uncer-tainty of speed distribution has already been considered, so we accept 1 dBaccuracy as suggested by [6]. Finally, we summarize all accuracy due to theuse of TransCAD output as IMMI input:

Problem Solution AccuracyTraffic apply distribution 0.5 dB

Composition based on measurementsEmission weight from [27] 1 dB

values and measurementsWeek-end flow same as weekdays 1 dB

Long term values apply distribution 1 dBfrom peak flow based on measurements

Chapter 7

Noise model implementation

7.1 Introduction

Noise mapping has been performed with prevision software IMMI version6.3.1 (powered by Woelfel and distributed by Microbel). This version isvery different from the one used for the first map. Not only bridge struc-tures were added, but also calculation is implemented in a more efficient way.Moreover, it’s now possible to distribute calculation on many PC withoutmanually sectioning the project (see section 7.3.3). All these modificationstogether with new streets and building elements induced us to create a newproject: therefore, all cartographic elements have to be inserted and con-trolled.After that, calculation parameters have to be set: notice that calculationparameters ought to be a balance between accuracy and run time possi-bilities, so we try to improve calculation respect to previous possibilities.Nevertheless, we have to take into account that a maximum of three PC canbe used to perform calculation.All assumptions and modelling choices will be described in the last sectionto evaluate accuracy of noise model.

7.2 3D model implementation

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

62

CHAPTER 7. NOISE MODEL IMPLEMENTATION 63

which have absolute one. Moreover, it provides also street lines, foot-pathlines, viaducts and bridge lines but their absolute height is not always reli-able.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: thatmeans all available lines and ground points with an absolute height greaterthan 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 wateringcanals).

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 viaductsor bridges instead of ground one: points have been cancelled;

• geodesic, railway, streets or foot-path lines and altitude points nearbuildings with gutter instead of terrain elevation: points and lineshave been cancelled;

• hydrological lines only on one river side: hydrological lines have beensupposed equal to the other side and copied;

• lines partly wrong because of previous errors: lines have been split andthe 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 selectfeatures from the project during the 3D view and it put them on a separatecollection to be elaborated.

7.2.2 Bridges and viaducts

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


barriers can be associated. However, not all viaducts or bridges are well de-fined in regional cartography; in fact, some bridges and viaducts have beencreated 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 includinggarden ones. Unfortunately, regional cartography gives relative height thatis not always reliable; furthermore, it distinguishes between concrete, drywall and fences but also this characteristic is not reliable.Therefore, we decided to consider and control only principal walls in thetown:

• city wall;

• military zones walls;

• botanical gardens walls;

• prison walls;

• Cottolengo walls;

• river parapets.

Moreover, almost all sound barriers have been considered: they are classifiedas wall whose absorption coefficient is very high. Some barriers height wereavailable, others have been estimated by visual inspection with the aid of3D 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 adaptingthem 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 andmodified inserting nodes to let the streets fit terrain profile.


Figure 7.1: Streets adaptation to DTM

Of course streets on bridge or viaducts don’t follow terrain profile butbridge one: software provides an automatic tool to adapt sound sources onBRUCK elements. This tool has been used ensuring that a sufficient numberof nodes are included and well positioned in the considered street, otherwisethis 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 aremore reliable; moreover, detailed cartography includes balconies and otherspreading elements that could complicate calculation. However, looking atthe project, we realized that this layer lacks of many new buildings so westarted using GoogleEarth to create new buildings. IMMI provides importof GoogleEarth georeferred maps on the project so that it’s possible to see


both real photo and project elements. Therefore, we start adding new build-ings (with height based on number of floors seen with Live Search Map) butduring this process a new 1:2.000 layer was produced: we decided to insertnew 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 be-cause this new layer is still less reliable than the other, especially in historicalcentre where no new buildings have to be added.In addition to new buildings identification, GoogleEarth tool has been usedto update buildings use: regional cartography provides different buildingscodes to identify their use but sometimes this could be different from realone (e.g. cemetery is coded as residential building). Codes distinguish be-tween residential, industry, religious, and also other types of elements likehothouses, penthouses, huts that haven’t been considered.Finally, GoogleEarth has been used also to set streets on centre line: es-pecially San Piero Fi-Pi-Li turn off and Via Moruzzi at the limits of thetown were designed through this tool because no updated cartography wasavailable.Other settings regarding buildings and streets are:

• road surface has been considered as normal asphalt for all streets (ex-cept streets were absorbent asphalt has been recently installed);

• circulation on road has been considered fluid and continuous withspeed from traffic model for day and evening period and speed limitduring night period (emission sound in these conditions is highlightedin figure 7.3);

• minimum buildings height has been set to 3 m;

• buildings with area less than 20 m2 have been cancelled.


Figure 7.3: Sound emission levels for fluid continuous circulation


7.3 Calculation settings

Calculation settings menu is divided into global parameters tab, elementsparameters tab and calculation model tab. Elements whose parameters haveto be set are streets, buildings and walls. Global parameters include tem-perature and meteorological conditions and other settings useful to managepopulation exposure without having data per building (it’s not this sit-uation). Anyway, it’s more useful to analyse settings divided into sourcesand propagation settings because parameters of different tabs influence eachother. 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 pointsources: as presented in chapter 3, French splitting methods are three(equiangular, constant and variable step), however this software implementonly equiangular one.Equiangular step depends on receivers position and the equivalent lengthassociated with point source is l ≤ αd, in which d is direct distance source-receiver and α is a parameter whose standard value is 0.5 but could bechanged to increase number of sources. This distance criterion factor hasbeen increased to manage free field calculation: in that case, otherwise,buildings distance would be too large and sources too much far from eachother (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 sourceis not considered: this radius has been set to 500 m for all streets apart fromA12 highway whose radius has been set to 1.5 km. In fact, urban streets arevery close to buildings and their noise cannot reach greater distance withoutloosing much power (due to reflection and absorption); instead, A12 is infree field condition and its power is very high so it could be heard at very


long distances.Finally, source meteorological correction has been taken from GPG becauseno specific data were available. So probability of favourable conditions hasbeen set to 50%− 75%− 100% respectively for day, evening and night peri-ods. Other global meteorological settings regard temperature and humidityto calculate atmospheric absorption (a propagation issue): average temper-ature 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 secondorder but run time increases exponentially and it was impossible to performsecond order calculation on the entire municipality.Reflections have been activated for all vertical and horizontal elements, soan 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 barrierswhose coefficients have been expressed in dB and calculated from availablemeasurements.Finally, ground absorption has to be set: tool 13.1 indicates 0.5 factor forresidential areas and the whole municipality has been supposed to be likethat.

7.3.3 Automated distributed calculation: segmentation

The program system of IMMI [34] provides the possibility of efficiently edit-ing very large models and comprehensive grid calculations by means of the“AUDINOM – distributed grid calculation” module. The module is able todistribute calculation over several computers: in fact, project is automati-cally 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 build-ing and running a unique project over the entire municipality.Calculation has been distributed over three PC (maximum of licenses avail-able): project is divided in several segments overlapping each other in alldirections. Segmentation 9x9 has been carried out with an overlapping bufferof 500 m: buffers role is to include in the calculation area of each segmentalso other sources of neighbour segments whose power influences levels ofconsidered segment. Notice that, for each segment, model doesn’t estimatenoise levels in buffers but only inside the segment; after all segments arecalculated, global grid is automatic assembled.Segmentation is a physical division of the project, so it’s possible that asegment does not include any sources in the buffers: here levels could not becalculated because there is no knowledge of other far sources. This problem


occurred in the area of S. Rossore Park: however, traffic noise is not zero be-cause 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 kmhas been defined to include both areas and sources and only one segmenthas been calculated to cover this specific area.Finally, we must underline that few areas haven’t been calculated becausesources action radius ended before reach them: in these cases interpolationhas been performed.

7.3.4 Grid resolution

First map implementation performed a 10 m step grid: at the beginning ofthe project we would obtain 2 m grid step to improve resolution. Beforetrying this step over the whole project, we tested differences between gridsteps: we consider a segment in the area of Tirrenia (about 2.8 km x 8.5 kmlarge) 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 Points Time (minuts)20 m 60489 6510 m 241674 2555 m 964419 10202 m 6019086 6316

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


Of course slow steps are more reliable in urbanized context: a street isusually 6-8 m large, so 10 m step may calculate a point on the street oron the buildings instead 5 m one can calculate both. In following figureswe compare grids in a specific area: we notice that 20 m grid is unable torepresent noise correctly (an average area of 400 m2 is too large) insteadother grids are quite similar. However, 10 m grid is unable to estimatecorrectly back facades levels, so it’s been excluded.From this example, we could assume that performing 2 m calculation overall municipality (15 km x 18.8 km) would take about 49 days: of course thistime could be lowered considering segmentation on many PC; however, citycontext is more complicated so we decided to perform 5 m step grid (whichshould take 8 days on a single processor).Finally, 5 m step grid has been performed with AUDINOM on three PC (2dual core with Windows XP, and a Pentium 4 with Windows 2000) and ittakes 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)


7.4 Facade calculation and population exposure

Population data of census 2001 have been elaborated by arch. C.Chiari andGrad. A.Panicucci to obtain number of inhabitants per building: in fact,they had to establish priority index defined by DM Ambiente 29/11/00 toassign action plans priorities. This elaboration is based on guidelines APAT[35] method b) which is the same suggested in deliverable 8 of IMAGINEproject [36] (procedure F.b): population per building is taken proportionalto 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 useis performed, so an accurate control of schools and hospitals has been done.This elaboration considered residential buildings of new 1:2.000 layer. Aswe already explained that’s not the same used in IMMI project; however,population per building was there available and facade calculation have beenperformed only for these sensitive buildings (excluding industrial).This layer has been inserted into the project with population information to-gether with codes to distinguish schools, hospitals from houses. Then IMMIperforms facade calculation interpolating grids: levels have been calculatedon a points ring around each building (2 m far from facade and 2.5 m farfrom each other).Maximum and minimum levels are evaluated to establish if a quiet facade1 ispresent. Then population exposure is calculated considering all inhabitantsexposed to maximum level as established by the END.Notice that facade calculation according the END doesn’t consider reflectedsound, but only incident one: reason is that people are not affected by soundreflected from the building, so 3 dB are subtracted by default when gridsare interpolated. This subtraction suppose that facade are completely re-flective: despite this, our settings are different because absorption coefficientis not zero. Therefore, for an incident level of 60 dB we have 59 dB reflectedand total is 62.5 dB and so subtraction of 3 dB underestimate exposure of0.5 dB.

7.5 Accuracy evaluation

Accuracy of noise levels is estimated with GPG toolkits. Source relatedtoolkits not yet considered are toolkits 5 and 7 regarding road surface andgradient. Surfaces of Pisa streets are all normal asphalt apart from streetsnear two schools were absorbent asphalt is in use: so we assume an accuracyequal to 1 dB from tool 5.2 in which surface correction is based on physicalproperties. Road gradient is estimated from DTM because streets lines fit

1By definition from annex 6 of DL 19/08/05 a quiet facade is one whose level is 20 dBlower than the higher facade level.


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 bynecessity to check them (accuracy greater than 0.5 dB, tool 12.1).Toolkit 13 regards how surface absorption is defined: we consider the wholemunicipality as residential area so tool 13.1 gives 1 dB accuracy.Toolkits 14-15 regards elements height: building height is known, so nouncertainty is given. However, height data from cartography comes fromaerial photos so 1 dB accuracy should be considered according tool 15.2;instead, barriers (more generally walls) height is not always known correctlyso we verified them by visual inspection. This last method accuracy is 1 dBaccording tool 14.2; however, considering that most walls are correct andknown from regional cartography, we assumed 0.5 dB accuracy.Toolkit 16 regards absorption of vertical elements: we assumed suggestedabsorption coefficients so 1 dB accuracy has to be considered.Toolkits 17-18 regard meteorological condition but they express quality eval-uation: we have low accuracy due to lack of local data.Finally, GPG gives qualitative accuracy of inhabitants estimation per build-ing and per dwelling: as already said, according IMAGINE evaluation, ourprocedure is the best with available data. Furthermore, quality is good be-cause we distribute population considering use of building: in addiction tothis, 1:2.000 cartography distinguish between dwellings units so if we have abuilding which use is both commercial and residential, people are distributedproportional to residential volume.

Chapter 8

Noise mapping results

8.1 Noise road map

In previous chapters we explained how segmentation has been done and howwe obtained global map. We will present all maps in appendix B: noticethat there is a map for each period (day evening and night) and also onewith LDEN indicator and one with Italian diurnal indicator. Here we showonly maps of LDEN and LNight indicators (figures 8.1 and 8.2).Together with whole municipality maps some detailed maps in appendix Bshow city centre, hospitals areas and an example of residential area.From whole municipality maps could be identified quiet area of S. RossorePark on north-west and military areas on south-west; it’s clear that higherlevels 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 themunicipality because population density is low too.

74

CHAPTER 8. NOISE MAPPING RESULTS 75

Figure 8.1: Roads traffic LDEN levels


Figure 8.2: Roads traffic LNight levels (22.00-6.00)


8.2 Population exposure to road noise

Population exposure has been evaluated according annex 6 of END: numberof inhabitants exposed to levels of LDEN higher than 55 dB and LNighthigher 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 facades is shown (see figure 8.4).

Figure 8.4: Inhabitants with quiet facades (road noise)

Schools and university departments exposure has been analysed (see figure8.5) 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 mustconsider that most exposed buildings belong to S. Chiara Hospital which isgoing to be dismissed, so exposure is going to be better.Finally, also percentage distribution is shown for Italian indicators in figure8.7: this kind of graphic underlines also good levels to have an idea of theentire municipality.


Figure 8.5: School buildings exposed to diurnal levels

Figure 8.6: Hospital buildings exposure

Figure 8.7: Distribution of exposed population


8.3 Accuracy results

As already said, accuracy of maps presented is evaluated both theoreticallyand through measurements comparison. In following sections we summarizechoices and we calculate global uncertainty, then we compare calculated andmeasured 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 modeland more generally to input data which are different for TransCAD roads(flow from model), classified roads (flow from first map) and for night esti-mation; others related to noise model implementation. These choices andtheir accuracy are shown in following figure, together with toolkits used.


Notice that in GPG is given the following explanation related to uncer-tainty of each tool:“The quantified accuracy statements presented within the toolkits representthe likely level of acoustic uncertainty introduced into the result by the useof that toolkit option, with a 95% confidence level. It must be noticed, thatthis represents the uncertainty of the total results only if all other input datais accurate. If there is uncertainty in any, or all, other input datasets, thenthe research concludes that total uncertainty in the receptor result level willbe 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 un-certainties has been performed. In fact, distribution of possible errors dueto a single choice is supposed to be independent1 from other ones: there-fore superposition of distributions should be a Normal distribution with σtobtained squaring single uncertainties2:

σt =√∑

i

σ2i

So global obtained theoretical uncertainties for different periods and streetsare listed below:

Table 8.1: Uncertainties of noise levelsTransCAD Classified roads

Day 3.1 dB 4.0 dBNight 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 traf-fic noise measurements. This means that we included only measurementcampaigns whose aims were to detect noise from road infrastructures orenvironmental noise campaigns whose positions were far from other soundsources. Moreover, measurements have been excluded whenever traffic con-ditions have changed (new one way streets, new ZTL, new traffic circles. . . ).Of course also measurements have an uncertainty that ought to be includedbefore analysing residual distribution. This kind of uncertainty is due to

1Speed and flow for modelled roads are obviously not independent but their covarianceis negligible.

2Square sum corresponds to 95% boundary so 1.96σ values is calculated.


instrumental chain, to operating conditions (time variability of source),whether conditions and residual sound (according ISO 1996-2, [37]): we canneglect all uncertainties but instrumental because measurements are justlong term evaluated and flows are high so that operating conditions aren’tinfluential. 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 togetherwith global uncertainty on levels:

σtot =√σ2t + σ2

m

Global uncertainty at 95% of differences distribution is evaluated and listedbelow:

Table 8.2: Uncertainty of residuals distributionsTransCAD Classified roads

Day 3.7 dB 4.5 dBNight 4.0 dB 4.8 dB

Measurements include both continuous and spot positions because nightlevels 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


Figure 8.9: Measurements south positions

8.3.3 Residuals distributions

We explained that roads have been modelled in two different ways and accu-racy has been evaluated separately, so also residual distribution is supposedto be different for each kind of road. We extracted two data sets from avail-able measurements: points in proximity of TransCAD modelled roads andones 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 thefirst set 95% of diurnal and 92% nocturnal samples are within expectedvalues (95% is within 4.3 dB). The first data set is quite large so we couldalso fit the distribution with a Normal distribution and analyse estimatedparameters.Therefore, we applied following function to data set:

f(x) = ae−(x−bc )2

being Normal function:

N =1√2πσ

e−

(x−µ√

2σ

)2

then σ = c/√

2 and 95% fitted confidence level is given by 1.96/√

2c. Fitsare shown in figure 8.10.

3Measurements near junctions of different types have been here ignored.


Figure 8.10: Diurnal and nocturnal distributions of TransCAD data set

Fits show that 95% confidence level value of diurnal distribution is 3.7 dBand night distribution fit returns 4.0 dB that are the same as previewed, sowe have a perfect correspondence between fits and GPG forecasts.Instead of 4.5 dB, 4.7 dB is estimated value for day distribution on classifiedroads (for nocturnal it’s 5.5 instead of 4.8): however, this data set is quitesmall and distribution is not really a Gaussian because it’s not symmetric.Therefore, real dispersion is smaller than fitted one: in fact, 89% of diurnaldata and 80% of nocturnal one are within expected values and only fewmeasurement points are outside expected boundary.Moreover, distributions are not central: diurnal levels calculations of bothsets seem to overestimate measured values (medians are −0.1 and −0.4);instead nocturnal sets have opposite medians (−0.8 and 0.8). This willproduce a broader distribution on global levels: in particular for diurnaldistribution we expect uncertainty similar to classified data set (it’s thelarger one and it includes the other one); instead, nocturnal distributionwill be broader than previous ones. Fitting global data (including ones atjunctions) we obtain 4.3 dB for diurnal distribution and 5.5 dB for nocturnalthat is what we were expecting (see fits in figure 8.11). We show also fitsresults in terms of estimated parameters, 95% convidence level and adjustedroot square. Notice that b is average of the fitted curve and a is percentageof discards which are less than 1 dB: we can observe that levels of classifiedrods are overestimated (averages are negative), instead of TransCAD oneswhose distributions averages are positive.


Figure 8.11: Diurnal and nocturnal global distributions

Table 8.3: Fits resultsFit global global TransCAD TransCAD classified classified

diurnal nocturnal diurnal nocturnal diurnal nocturnala 0.34 0.28 0.42 0.38 0.31 0.27

b [dB] 0.06 0.49 0.07 0.84 -0.32 -1.03c [dB] 3.1 4.0 2.6 2.9 3.4 4.095% 4.3 5.5 3.7 4.0 4.7 5.5r-sq. 0.99 0.99 1.00 0.98 0.91 0.78

Therefore, we can assert that GPG is able to predict accuracy in a re-liable way but we have to consider uncertainties due to sound power levelsand to flow period distribution. We want to underline that accuracy on cal-culated levels are the ones in table 8.1. Moreover, we could estimate globalaccuracy subtracting measurements uncertainty from obtained values: wehave about 3.8 dB for diurnal values and 5.1 dB for nocturnal.Accuracy has been improved (see next section) on levels but main problemremains nocturnal values: in fact, we obtained that one method underes-timates and the other overestimates producing a broader distribution. Apossible improvement is to estimate correctly time coefficients for road traf-fic flow during night especially on classified roads (overestimation of valueson classified roads is due to an overestimation of flow); furthermore, we couldimprove accuracy measuring speed during night because, especially on mainroads, speed limits are often exceeded (that explains underestimation ofnight levels on TransCAD roads).


8.4 Comparison with previous map results

If we compare first map with this one, we notice that residual distributionhas been improved: in fact, distribution width is smaller. Notice that thisimprovement is not only due to traffic model (previous distribution is largereven than classified one), but also to a more accurate 3D model. Despitepreviewed nocturnal accuracy is quite the same of first map, residual distri-bution is better (see figure 8.12): that’s another evidence of a more accuratemodel.

Figure 8.12: Residual distributions

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


then residential population was calculated based on volume proportion be-tween the considered building and all residential buildings in that censuszone (ISTAT cell, census 1991).Population exposure has been calculated both with Italian and Europeanindicators: notice that LDEN and LNight consider only incident sound. Weprefer to compare distributions according diurnal and nocturnal Italian in-dicators (figure 8.13) because they are not corrected for reflections and socorrespond to grid values.People exposure is more critical according new project: this fact could bedue to walls modelling (garden walls not included), but also to more real-istic traffic flows. Anyway, if we consider number of exposed citizens, thisis quite the same because city population decreased. In fact, populationexposed to nocturnal levels higher than 50 dB increased of 4100 inhabitantsand to diurnal levels higher than 55 dB decreased of 5700 inhabitants.

Figure 8.13: Population comparisons


8.5 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 INMand railway noise has been calculated by Grad. Cerchiai and Panicucci withsoftware SoundPlan. These maps have been exported as ASCII grids andthen transformed into IMMI format so that energy sum could be performedaccording next equation:

L = 10 log(

10La10 + 10

Lr10 + 10

Ls10

)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 thewhole municipality, but only where it’s audible (i.e. till 2 km far from rail-way lines).Railway noise has important effects on west part of municipality where thereis Torino-Palermo line, but it’s not dominant because it’s parallel to high-way A12 and to S.S. Aurelia. Other important effects are due to Pisa-Florence line on east part because many buildings are close to railway.Aircraft noise is prevalent on south-west part of municipality because de-partures and arrivals go along the same routes so that most populated partof the city is not affected. Of course road noise is prevalent in all othersituations in the town.Accuracy of strategic map has been verified by Grad. Panicucci in [38]:global accuracy depends obviously on which source is prevalent (accordingnext equation) but, if all sources are comparable, uncertainty is the biggestone between sources.

∆LG =

√100.2La∆L2

a + 100.2Lr∆L2r + 100.2Ls∆L2

s

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, wecan 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 sameas road map.Strategic noise maps and single sources maps are shown in appendix C, herewe show only LDEN and LNigth maps (figures 8.14 and 8.15).

4Department for Environment, Food and Rural Affairs of United Kingdom.


Figure 8.14: Strategic LDEN levels


Figure 8.15: Strategic Night levels (22.00-6.00)


8.5.1 People exposure to global levels

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

Figure 8.16: Inhabitants exposure

Figure 8.17: Inhabitants with quiet facades

8.5.2 Conflicts maps

In addition to people exposure, we elaborated conflicts maps: these mapsare an efficient instrument to show critical areas. In fact, they show dif-ferences between law limits and calculated levels. Nowadays there aren’tlimits for European indicators, so in Italy we elaborate conflicts maps forItalian indicators [42] whose limits are established not only by PCCA, butalso 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


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 soit’s a very annoyed area especially during night period. Other critical areasare a sector of S. Rossore Park, because of A12 highway noise in a very lowlimits zone, and south area next to railway and A12 which has low popu-lation density. These maps will be useful to manage action plans: PCRAestablishes priority index based upon receiver type (school, hospital, house)and how much limits are exceeded, therefore is essential to know differencesat receivers.


Figure 8.18: Diurnal limits


Figure 8.19: Nocturnal limits


Figure 8.20: Diurnal differences between levels and limits


Figure 8.21: Nocturnal differences between levels and limits


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 anddevelopments

This work has been carried out in Pisa ARPAT department and it producedPisa road noise map according European Noise Directive 2002/49/EC whichhas been transposed in Italy by D.L. n.194, 19/8/05.A new approach for traffic flow estimation has been performed: TransCADsoftware has been used and an innovative technique has been developed toimplement it in noise mapping procedure. We verified that noise mappingaccuracy has been improved, according forecasts of Good Practice Guideand we produced new information about speed and flow starting from alimited number of measurements.Traffic flow measurements have been used to calibrate model and then toverify reliability of estimated flow. A procedure has been tested and veri-fied to use passenger car equivalent flow into the traffic model and then toprovide NMPB vehicles categories requested by the END.Moreover, traffic measurements have been used together with sound levelsones to calibrate noise model; finally, we validated noise estimation withavailable measurements and theoretical considerations.This calibrated model is able to predict both traffic and noise hot spots withgood accuracy: actually it’s possible to obtain sound levels over the wholemunicipality with following accuracy: approx 66% of diurnal and nocturnalvalues are far from measured values less than 2.6 dB. These values confirmGPG suggested uncertainty: it means that future improvements should tryto modify modelling methods which have an high influence over global un-certainty (i.e. the ones which contribute with more than 1 dB). In fact, it’sclear from GPG toolkits that low flow roads and especially speed estimationare critical problems.Possible improvement is to include night flow estimation into traffic model;this choice it’s possible only through traffic measurement campaigns on sam-ple roads during night time. These future campaigns should include speed

97

CHAPTER 9. CONCLUSIONS AND DEVELOPMENTS 98

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

Appendix A

Acoustics basics

Sound Pressure level (SPL) Lp is a logarithmic measure of the root meansquare (rms) sound pressure of a sound relative to a reference value. Soundpressure is the local pressure variation from the atmospheric room pressurecaused 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 mov-ing 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:

fx = −∂p∂xdx(dydz)

This force is balanced by acceleration so motion law is given by:

∂p

∂xV0 = −ρV0

duxdt

Density doesn’t usually vary too much so we can write previous equation as:

∂p

∂x= −ρ0

∂ux∂t

Moreover, if we consider all directions, we obtain Eulero equation:

grad p = −ρ0∂~u

∂t

However, motion induces a mass variation inside the volume but accordingmass 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 veryfast so process is considered adiabatic so that following equation is given:

1p0

∂p

∂t= γ

∂δ

∂t= −γdiv ~u

in which p0 is static pressure and γ = cp/cv is specific heat ratio. Therefore,wave equation is calculated:

∇2p =1c2

∂2p

∂t2

99

APPENDIX A. ACOUSTICS BASICS 100

in which c is sound speed defined by:

c =√γp0

ρ0

RMS sound pressure is the root mean square of instantaneous one over agiven interval of time. SPL is measured in decibels (dB) and reference soundpressure is 20µPa which is considered as the threshold of human hearing.So SPL is given from the following expression:

Lp = 10 log(p2rms

p20

)At the same way, sound power level is given for a reference sound power of10−12 W:

LW = 10 log(W

W0

)Sound power level (measured one meter far from source) is related to soundpressure level so that for a point source in free field condition and for roomtemperature (i.e. when ρ0c is 400Pa · 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 differentfrequencies is heard at different loudness: therefore, it’s been defined equalloudness contours (expressed in Phon) which are a family of curves functionsof frequency.In particular the 40 dB curve is called A-weighting and it’s used to correctSPL to mirror real human sensation. So, environmental sound pressure levelis usually1 expressed in dB(A) where A-weighting correction is applied toeach octave band.Environmental noise is estimated through A-weighted equivalent level whichcorresponds to a constant hypothetical source whose sound energy is thesame of real time-varying sound:

LAeq = 10 log[

1T

∫ T

0

p2A(t)p2

0

dt

]Notice that T is time reference interval and it’s usually an hour so we speakabout LAeq,h.Another important indicator is Sound Exposure Level SEL which is used

1Aircraft noise is weighted with 100 dB countours.

APPENDIX A. ACOUSTICS BASICS 101

to identify contribution of single events: it’s the level that the event wouldassume if all his energy would be concentrated in one second

SEL = LAeq + 10 logTe

1sec

in which Te is real event time length.Law limits in Italy are expressed as average diurnal and nocturnal valuesof LAeq,h: this means that we estimate average hourly equivalent level overdiurnal period (6.00:22.00) and nocturnal period (22.00:6.00).

LD = 10 log

[116

∑i

10LAeq,hi

10

]

LN = 10 log

[18

∑i

10LAeq,hi

10

]in which i varies along hours.The END instead establishes different indicators: in addiction to LN calledLNight, it defines LDEN as a more complete indicator defined by the followingexpression (time periods adapted according DL n.194, 19/08/05):

LDEN = 10 log124

(14 · 10

Lday10 + 2 · 10

Levening+5

10 + 8 · 10Lnight+10

10

)in which Lday, Levening, Lnight are the A-weighted long-term average soundlevel 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 consideronly incident sound, this means subtraction of 3 dB must be done measuringfacade 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 LNight 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 ofbuildings 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 powerlevels and it might affect the area.Moreover, Cisanello hospital lies actually in a quiet area but with the futureenlargement a new viability is previewed: therefore, administrations shouldpay attention to position of hospital rooms and major access roads.

102

APPENDIX B. ROAD NOISE MAPS 103

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


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


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


Figure B.4: Road traffic noise LDEN levels

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

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. Weshow day, evening and diurnal strategic maps (not shown in previous chap-ters) in figures C.1-C.3 and maps of aircraft (figures C.4-C.6) and railwaynoise (figures C.7-C.9) in terms of Diurnal, DEN and Night levels.

110

APPENDIX C. STRATEGIC NOISE MAPS 111

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


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


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


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


Figure C.5: Aircraft LDEN levels


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


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


Figure C.8: Railway LDEN levels


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

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