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Arnaud Can, Université de Lyon, ENTPE/INRETS, LICIT

Influence of noise source representation on the estimation of specific descriptors close to traffic signals. Arnaud Can, Université de Lyon, ENTPE/INRETS, LICIT Ludovic Leclercq, Université de Lyon, ENTPE/INRETS, LICIT Joël Lelong, INRETS, LTE. Background. L Aeq. time. V i-1 ,Q i-1.

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Arnaud Can, Université de Lyon, ENTPE/INRETS, LICIT

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  1. Influence of noise source representation on the estimation of specific descriptors close to traffic signals Arnaud Can, Université de Lyon, ENTPE/INRETS, LICIT Ludovic Leclercq, Université de Lyon, ENTPE/INRETS, LICIT Joël Lelong, INRETS, LTE

  2. Background LAeq time Vi-1 ,Qi-1 Vi+1 ,Qi+1 Vi ,Qi Lwi Lwi+1 Lwi-1 P Dynamic model Static model Network Dynamic traffic flow model Static traffic flow model V(t), x(t), a(t) V,Q Vehicle noise emission estimation Cells noise emission estimation Lw,vehicles(t) Cell noise emission estimation Lw,cells(t) Lw,cells Sound propagation calculation Sound propagation calculation LAeq,1s(t) at receiver Noise descriptors calculation Noise descriptors calculation LAeq at receiver Whatever descriptor calculated from LAeq,1s evolution

  3. Objectives Dynamic model Macroscopic car following model v(t), x(t), a(t) Vehicle noise estimation Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Sound propagation calculation Which noise source representation is relevant ? LAeq,1s(t) Noise descriptors calculation

  4. Dynamic traffic flow model Macroscopic car following model is sufficient for specific descriptors estimation Dynamic model Previous results • Macroscopic models [Leclercq-2002] • Car following models [De Coensel et al.-2005] ; [Leclercq et al. -2007] Macroscopic car following model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise estimation Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Which noise source representation is relevant ? Sound propagation calculation LAeq,1s(t) Noise descriptors calculation

  5. Macroscopic car-following model x i-1 i Qx Flow Q (veh/s) -w Vx free flow congested flow Kmax=1/smin Kc Density K (veh/m) smin

  6. Macroscopic car-following model vx xi (t+Δt) Qx Flow Q (veh/s) -w Vx x xi (t) free flow congested flow Kmax=1/smin Kc Density K (veh/m) demand term smin

  7. Macroscopic car-following model xi(t) xi(t+Δt) Qx smin Flow Q (veh/s) -w x xi-1(t) Vx free flow congested flow Kmax=1/smin Kc Density K (veh/m) supply term

  8. Dynamic traffic flow model Dynamic model Macroscopic car following model Macroscopic car-following model v(t), x(t), a(t) Vehicle noise estimation Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Which noise source representation is relevant ? Sound propagation calculation LAeq,1s(t) Noise descriptors calculation 8

  9. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise estimation Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Which noise source representation is relevant ? Sound propagation calculation LAeq,1s(t) Noise descriptors calculation 9

  10. Noise emission law 10 smin

  11. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise emission estimation : Lw=f(V,a) Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Which noise source representation is relevant ? Sound propagation calculation LAeq,1s(t) Noise descriptors calculation 11

  12. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise emission estimation : Lw=f(V,an Lw,vehicles(t) Cells noise emission estimation Lw,cells(t) Which noise source representation is relevant ? Sound propagation calculation LAeq,1s(t) Noise descriptors calculation 12

  13. d xi(t) xi(t+Δt) xi-1(t+Δt) Noise source representations • Reference: vehicle line source representation x xi-1(t) αi αi-1 P

  14. xi(t) xi-2(t) xi-1(t) d P Noise source representations • Reference: vehicle line source representation • Aggregation on fixed cells required for sound propagation calculation L cellj cellj-1 x αj αj-1 in phase opposed

  15. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise emission estimation : Lw=f(V,an Lw,vehicles(t) Cells noise emission estimation Which noise source is relevant ? Lw,cells(t) Influence of alignment ? Sound propagation calculation Which cell length ? LAeq,1s(t) Noise descriptors calculation 15

  16. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise emission estimation : Lw=f(V,an Lw,vehicles(t) Cells noise emission estimation Which noise source is relevant ? Lw,cells(t) Influence of alignment ? Sound propagation calculation Geometric attenuation Which cell length ? LAeq,1s(t) Noise descriptors calculation 16

  17. Dynamic traffic flow model Dynamic model Dynamic traffic flow model v(t), x(t), a(t) Vehicle noise emission estimation : Lw=f(V,an Lw,vehicles(t) Cells noise emission estimation Which noise source is relevant ? Lw,cells(t) Influence of alignment ? Sound propagation calculation Which cell length ? LAeq,1s(t) • Whatever descriptor calculated from LAeq,1s evolution Noise descriptors calculation Specific descriptors Proposed in [Can et al., 2008] 17

  18. LS7m L=7m LS14m LS28m L=14m L=28m Method • Scenario tgreen=60s tred=30s Q=900veh/h x d=5.5m x=28m x=-28m x=-21m x=-14m x=-7m x=0m x=7m x=14m x=21m • Cell lengths: 7m, 14m, 28m • x=+14m, LS14m vs LS28m • d: 5.5m, (+ d=10m in proceedings) • Receiver positions: -28m, -21m, -14m, -7m, 0m, 7m, 14m, 21m, 28m • Maximum errors: -28m, -21m, -14m, -7m, 0m, 7m, 14m, 21m, 28m • Noise descriptors: LAeq, L1, L10, L50, L90, L’green, L’red

  19. LS14m vs LS28m • LS28 overstimates red level High length mixes different traffic states • LS28 looses dynamics from vehicles motion Noise source representation should be chosen to ensure a precise estimation at any receiver location

  20. Main results • LAeq estimation: all lengths suitable • 14m length sufficient for all descriptors estimation except L1 and L10 if 2dB(A) error admitted • 7m length guarantees: • all descriptors estimation with error under 2dB(A) • all descriptors estimation except L1 and L10 with error under 1dB(A)

  21. Conclusion • Alignment can affect estimation Noise source representation should be chosen to ensure a precise estimation at any receiver location • L10 & L1 not precisely estimated • Need to test traffic representations for specific descriptors estimation 21

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