arnaud can, université de lyon, entpe/inrets, licit
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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