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, LICITLudovic Leclercq, Université de Lyon, ENTPE/INRETS, LICIT

Joël Lelong, INRETS, LTE

Background Results Conclusion

2

Background

Noise descriptors calculation

V(t), x(t), a(t)

LAeq,1s(t) at receiver

Dynamic model

Static traffic flow model

Sound propagation calculation

Cells noise emission

estimation

Lw,cells(t)

LAeq at receiver

V,Q

Lw,cells

Static model

Whatever descriptor calculated from LAeq,1s evolution

Vi ,QiVi+1 ,Qi+1Vi-1 ,Qi-1 Lwi Lwi+1Lwi-1

P

time

LAeq

Network

Lw,vehicles(t)

Dynamic traffic flow model

Vehicle noise emissionestimation

Cell noise emissionestimation

Sound propagationcalculation

Noise descriptorscalculation

Background Results Conclusion

Sound propagation calculation

3

Objectives

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Macroscopic car following model

Vehicle noiseestimation

Noise descriptorscalculation

Background Results Conclusion

Sound propagation calculation

4

Dynamic traffic flow model

Macroscopic car following model is sufficient for specific descriptors estimation

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Macroscopic models [Leclercq-2002] Car following models [De Coensel et al.-

2005] ; [Leclercq et al. -2007]

Previous resultsMacroscopic car following model

Dynamic traffic flow model

Vehicle noiseestimation

Noise descriptorscalculation

Background Results Conclusion

5

Macroscopic car-following model

1 minmin ,i i x ix t t x t V t x t s

Kmax=1/smin

Density K (veh/m)Flo

w Q

(veh

/s)

congestedflow

freeflow

Qx

Vx -w

Kc

i i-1 x

Background Results Conclusion

6

Macroscopic car-following model

1 minmin ,i i x ix t t x t V t x t s

Kmax=1/smin

Density K (veh/m)Flo

w Q

(veh

/s)

congestedflow

freeflow

Qx

Vx -w

Kc

demand term

xi (t) xxi (t+Δt)

vx

Background Results Conclusion

7

Macroscopic car-following model

1 minmin ,i i x ix t t x t V t x t s

supply term

Kmax=1/smin

Density K (veh/m)Flo

w Q

(veh

/s)

congestedflow

freeflow

Qx

Vx -w

Kc

xi(t) xxi-1(t)

smin

xi(t+Δt)

Background Results Conclusion

Sound propagation calculation

8

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Macroscopic car following model

Macroscopic car-following model

Vehicle noiseestimation

Noise descriptorscalculation

Background Results Conclusion

Sound propagation calculation

9

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noiseestimation

Noise descriptorscalculation

Background Results Conclusion

10

Noise emission law

Background Results Conclusion

Sound propagation calculation

11

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noise emissionestimation : Lw=f(V,a)

Noise descriptorscalculation

Background Results Conclusion

Sound propagation calculation

12

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation

Which noise source representation is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noise emissionestimation : Lw=f(V,a n

Noise descriptorscalculation

Background Results Conclusion

13

Noise source representations

xi(t) xxi-1(t)xi(t+Δt) xi-1(t+Δt)

P

αi αi-1

10 log 10 10 log(2 )10,1

Lw tiL t diAeq s

i

d

Reference: vehicle line source representation

Background Results Conclusion

14

Noise source representations

Reference: vehicle line source representation

Aggregation on fixed cells required for sound propagation calculation

xi(t) xi-1(t) x

P

xi-2(t)

αj αj-1

cellj-1cellj

d

10 log 10 10 log(2 )10,1

LW tjL t djAeq s

j

L

110 log 10 , : vehicles on the line source10

Lw tiLW t Ij Li I

opposedin phase

Background Results Conclusion

Sound propagation calculation

15

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation Which noise source is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noise emissionestimation : Lw=f(V,a n

Noise descriptorscalculation

Influence of alignment ?

Which cell length ?

Background Results Conclusion

Sound propagation calculation

16

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation Which noise source is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noise emissionestimation : Lw=f(V,a n

Noise descriptorscalculation

Influence of alignment ?

Which cell length ?Geometric attenuation

Background Results Conclusion

Sound propagation calculation

17

Dynamic traffic flow model

v(t), x(t), a(t)

Lw,vehicles(t)

LAeq,1s(t)

Dynamic model

Cells noise emission estimation Which noise source is relevant ?

Lw,cells(t)

Dynamic traffic flow model

Vehicle noise emissionestimation : Lw=f(V,a n

Noise descriptorscalculation

Influence of alignment ?

Which cell length ?

Whatever descriptor calculated from LAeq,1s evolution

Specific descriptorsProposed in [Can et al., 2008]

Background Results Conclusion

18

Method

x

Scenario

d=5.5m

Q=900veh/h

tgreen=60stred=30s

L=7mL=14mL=28m

Cell lengths: 7m, 14m, 28m

Receiver positions: -28m, -21m, -14m, -7m, 0m, 7m, 14m, 21m, 28m

x=-28m x=-21m x=-14m x=-7m x=0m x=7m x=14m x=21m x=28m

Noise descriptors: LAeq, L1, L10, L50, L90, L’green, L’red

d: 5.5m, (+ d=10m in proceedings)

LS7m

LS14m

LS28m

x=+14m, LS14m vs LS28m

Maximum errors: -28m, -21m, -14m, -7m, 0m, 7m, 14m, 21m, 28m

Background Results Conclusion

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

Background Results Conclusion

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)

Background Results Conclusion

21

Conclusion

Need to test traffic representations for specific descriptors estimation

Alignment can affect estimation

Noise source representation should be chosen to ensure a precise estimation at any receiver location

++ : error <1dB(A) + : error <2dB(A) - : error >2dB(A)

Cell length

28m 14m 7m

Distance from the road

10m + ++ ++

5.5m - + ++

L10 & L1 not precisely estimated