Acoustic descriptors for dynamic noise estimation close to traffic

signals

Arnaud Can, LICIT (ENTPE/INRETS)Ludovic Leclercq, LICIT (ENTPE/INRETS)

Joël Lelong, LTE (INRETS)

Can / Leclercq / Lelong 2

Introduction Existing descriptors New descriptors Conclusion

Introduction

Descriptors set by legislation can hardly capture urban traffic noise variations

Temporal noise structure influences urban soundscape quality

Dynamics noise models are now able to assess LAeq,1s evolution

Need descriptors that reflect noise dynamics

[Leclercq-2002] ; [De Coensel et al.-2005]

Can / Leclercq / Lelong 3

Introduction Existing descriptors New descriptors Conclusion

Outline Existing descriptors and urban traffic noise dynamics Show their weaknesses for noise dynamics assessment

New descriptors for urban traffic noise characterization Focus on noise variations at a signal-cycle scale Based on Mean noise pattern reconstitution Evaluation of noise variations around this pattern

Conclusion

Can / Leclercq / Lelong 4

Introduction Existing descriptors New descriptors Conclusion

Experiment

Traffic situation: in front of a traffic signal

Cours Lafayette, Lyon (France)Three lanes one way streetStreet quite busy (1400veh/hour)

Measurement: Acoustics: LAeq,1s evolution Traffic: tgreen=50s, tred=40s, flow rates

5

Existing descriptors and urban traffic noise dynamics

Can / Leclercq / Lelong 6

Introduction Existing descriptors New descriptors Conclusion

Existing descriptors and urban traffic noise dynamics

Limits of classical descriptors calculated over long period scales (24h)

Limits of classical descriptors calculated over short period scales

Unable to capture long-term or short-term noise variations ; see proceedings

Can / Leclercq / Lelong 7

Introduction Existing descriptors New descriptors Conclusion

Limits of classical descriptors calculated over short period scales

LAeq is too sensitive to peaks of noise

1%

+3dB

Can / Leclercq / Lelong 8

Introduction Existing descriptors New descriptors Conclusion

Limits of classical descriptors calculated over short period scales

Rhythm of noise at traffic signal scale is not captured by usual descriptors

Need specific descriptors

t = 90s traffic cycle duration

9

New descriptors for urban traffic noise characterization

Can / Leclercq / Lelong 10

Introduction Existing descriptors New descriptors Conclusion

New descriptorsfor urban traffic noise characterization

Description of the mean noise pattern

statistical descriptors vs. mean noise pattern

LAeq,1s distribution

Noise variations around the mean noise pattern

Can / Leclercq / Lelong 11

Introduction Existing descriptors New descriptors Conclusion

Description of the mean noise pattern

How descriptors are related to these levels ? How estimate these two levels ?

Traffic noise alternates between two levels

Can / Leclercq / Lelong 12

Introduction Existing descriptors New descriptors Conclusion

Classical noise descriptors and mean noise pattern

Statistical descriptors are not related to mean noise pattern Lgreen and Lred do not reflect upper and lower levels

Can / Leclercq / Lelong 13

Introduction Existing descriptors New descriptors Conclusion

Study of noise distribution

Two modes that correspond to each traffic signal phase

How characterize this distribution ?

Can / Leclercq / Lelong 14

Introduction Existing descriptors New descriptors Conclusion

Study of noise distribution

Two modes Green and red phases

Difference between modes

Dynamics at

the traffic signal scale

Amplitude of modes

Which one is predominant

bi-gaussian function:

r²adj=0.9988

Need to study variations around the mean noise pattern

Standard deviation of

modes

Noise variations

within each mode

2 2

1 2

1 22 21 2

1 2

A Af x = exp + exp

2 2

x x x x

Can / Leclercq / Lelong 15

Introduction Existing descriptors New descriptors Conclusion

Noise variations around the mean noise pattern

disappearance of calm periods

intensity of peaks

Periodicity and intensity of peaks: NLmax>80

NL5>75

L5/cycle

Lmax/cycle

Rarefaction of calm periods: NLmin>60

NL95>65

L95/cycle

Lmin/cycle

Can / Leclercq / Lelong 16

Introduction Existing descriptors New descriptors Conclusion

Conclusion

Usual descriptors fail to capture urban noise dynamics When calculated over long period When calculated over short period

Noise dynamics at traffic signals may be characterized by the mean noise pattern

None usual descriptor is related to this pattern

Specific descriptors can be proposed: Bi-gaussian fit mean noise pattern Traffic-scaled variations descriptors variations around the mean noise

pattern

Can / Leclercq / Lelong 17

Introduction Existing descriptors New descriptors Conclusion

Further investigations

Method allows differentiation between noise situations:

Comparison between the point in front of a trafic cycle and a point between two traffic cycle : proceedings

Generalization on more complicated scenarios (calm point, close bus station, two ways street…)

18

Thank you for your attention

Can / Leclercq / Lelong 19

Introduction Existing descriptors New descriptors Conclusion

Limits of classical descriptors calculated over long period scales (24h)

Can / Leclercq / Lelong 20

Introduction Existing descriptors New descriptors Conclusion

Unable to capture long-term noise variations [Can-2007]

Characteristics of the time slot are not reflected by descriptors

LAeq and statistical descriptors 24h estimation vs LAeq1s evolution

Limits of classical descriptors calculated over long period scales (24h)