Acoustic descriptors for dynamic noise estimation close to traffic signals

1. Acoustic descriptors for dynamic noise estimation close to traffic signals Arnaud Can, LICIT (ENTPE/INRETS) Ludovic Leclercq, LICIT (ENTPE/INRETS) Joël Lelong, LTE (INRETS)

2. 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 [Leclercq-2002] ; [De Coensel et al.-2005] Need descriptors that reflect noise dynamics

3. 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

4. Experiment • Traffic situation: • in front of a traffic signal Cours Lafayette, Lyon (France) Three lanes one way street Street quite busy (1400veh/hour) • Measurement: • Acoustics: LAeq,1s evolution • Traffic: tgreen=50s,tred=40s, flow rates

5. Existing descriptors and urban traffic noise dynamics

6. 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-termor short-term noise variations ; see proceedings

7. Limits of classical descriptors calculated over short period scales +3dB 1% LAeq is too sensitive to peaks of noise

8. 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

10. 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

11. Description of the mean noise pattern • Traffic noise alternates between two levels • How descriptors are related to these levels ? • How estimate these two levels ?

12. 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

13. Study of noise distribution Two modes that correspond to each traffic signal phase How characterize this distribution ?

14. Study of noise distribution • bi-gaussian function: r²adj=0.9988 Need to study variations around the mean noise pattern

15. Noise variations around the mean noise pattern intensity of peaks Rarefaction of calm periods: • NLmin>60 • NL95>65 • L95/cycle • Lmin/cycle Periodicity and intensity of peaks: • NLmax>80 • NL5>75 • L5/cycle • Lmax/cycle disappearance of calm periods

16. 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

17. 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

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

20. Limits of classical descriptors calculated over long period scales (24h) • LAeq and statistical descriptors 24h estimation vs LAeq1s evolution • Unable to capture long-term noise variations [Can-2007] • Characteristics of the time slot are not reflected by descriptors