PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Dynamic approach for the study of the spatial impact of road traffic
noise at peak hours
Arnaud CAN1; Pierre AUMOND1; Cécile BECARIE2; Ludovic LECLERCQ2
1
2

IFSTTAR, CEREMA, UMRAE, F-44344 Bouguenais, France

Univ Lyon, ENTPE, IFSTTAR, LICIT UMR_T 9401, F-69518, LYON, France

ABSTRACT
A dynamic modelling chain, coupling the Symuvia dynamic traffic model (LICIT, Ifsttar / ENTPE) and theNoisemodelling noise level prediction tool (UMRAE, Ifsttar / Cerema), is developed in this paper to assess
the impact of road traffic on noise environments at the scale of an urban traffic network of about 10 km²
located in Lyon/Villeurbanne. The simulation, which reproduces 3 hours of a morning rush hour scenario,
highlights the increase in noise levels associated with the increase in travel demands. It also highlights thatthe increase in noise levels is not uniformly distributed over the network and has a greater impact on thestreets where traffic is reported. In addition, noise dynamics are modified: background noise is particularly
sensitive to the increase in vehicle density on the network, so periods of calm on the network are rare. A moredetailed analysis of this dynamic is possible locally through the analysis of the evolution of the LAeq,1s and thestudy of specific acoustic indicators. Desirable improvements to the model are discussed, with a view toassessing the currently unknown acoustic impact of road traffic management strategies at the scale of anurban network.
Keywords: Road traffic noise, Dynamic modelling chain, Congestion, Noise dynamics

1. INTRODUCTION
Urban congestion is a major issue as regarding the development of cities. Its cost in terms of timespent, particularly during the morning and evening rush hour, has been the subject of studies (1),giving rise, for example, to policies that spread travel demand. However, the estimation of theenvironmental externalities that congestion generates, in particular noise pollution, faces scientificobstacles that have not yet been resolved. Conventional approaches to traffic noise modelling do notmeet the criteria for evaluating mobility strategies, as they are limited to an overly aggregated traffic
description, which relies on average flows and speeds to estimate the sound power of vehicle flows.
These traditional models also make simplifying assumptions about the evolution of these flows
according to traffic conditions, taking no account, for example, of the assignment of road traffic(distribution of vehicles on the network according to traffic conditions). The detailed estimation of thenoise impacts associated with road traffic presupposes the use of: (i) traffic modelling sensitive totraffic conditions, i.e. reproducing vehicle kinematics (speeds and accelerations) and the dynamic
assignment of vehicles on the network according to traffic conditions (individual route selection
strategies), (ii) acoustic emission modeling itself sensitive to influential var iables (vehicle speeds andaccelerations, road fleet composition, etc.) (2).
Approaches to traffic noise modeling based on dynamic road traffic modeling have emerged overthe past 15 years (3-8). These modeling chains are based on the trajectories provided by the trafficmodel of the vehicles present on the network, i.e. their position, speed and acceleration at each time
step (typically 1 second). These data are used to estimate the sound power of each vehicle at each time
step; this is followed by an estimate of the temporal evolution of the noise levels in a receiver map after
a sound propagation calculation. Thus, unlike conventional so-called static approaches, this dynamic
modelling chain makes it possible to test the impact of traffic regulation stra tegies that modify vehicle1

arnaud.can@ifsttar.fr

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kinematics. It also allows the calculation of acoustic indicators describing changes in noise levels,
where static methods are limited to the estimation of aggregate noise indicators (e. g. annual averagenoise levels) (9). This contribution is significant because variations in noise levels, and in particularemergences, have a demonstrated impact on the perception of sound environments (10) as well as on
their health impacts (11).
The dynamic modelling chain has been used in the past to compare the acoustic impact of different
intersections (12,13), or different traffic light settings on urban corridors (14, 15). These initial studies
highlight that traffic control strategies limiting speed variations are beneficial from an acous tic pointof view, confirming the results obtained by observations (16). However, the impact of reassignment
phenomena, which are very present in the case of congested traffic conditions, has not yet been
analysed, as the studies carried out so far have been limited to networks covering only a few
intersections.
Recent advances in traffic modelling and acoustic calculations now allow the study of larger
networks. In this article, a coupling between a dynamic traffic model, Symuvia, and a noise mapping
model, Noisemodelling, is carried out and applied to an urban network located in the city of Lyon,
France, covering an area of about 10 km². At this scale, it is possible to study the spatial dimension ofurban congestion in terms of its impact on the noise environment.

2. METHODS
2.1 Modeling chain
The dynamic modelling chain implemented for road traffic noise estimation consists of a coupling
between the Symuvia dynamic traffic model and the Noisemodelling road traffic noise prediction
model.
The architecture of the coupling between the two models is an adaptation of the static models
conventionally used: where static models estimate the noise levels emitted by road traffic based on the
average flow rates and speeds of vehicle flows per road section, the dynamic a pproach is based on thevehicle trajectories estimated by the traffic model to determine at each time step the sound power of
each vehicle present on the network. The modelling chain is described in Figure 1.

Figure 1 – Dynamic noise modeling chain

2.2

Road traffic modeling

The platform on which traffic simulation is based, Symuvia, is dedicated to the analysis of dynamic892

traffic simulations. Symuvia includes a tool for editing simulation data sets (flow network andscenarios), a calculation module and tools for analyzing and reporting results. These components aredesigned to perform traffic simulations over several hours, for an urban area that can cover several km².
Symuvia's calculation core includes an assignment module (vehicle route calculation) and a flow
module (trajectory calculation). Details on the modeling can be found in (17, 18).
2.3 Noise modeling
NoiseModelling is a free and open-source module of the OrbisGis Geographic Information System,
whose initial objective is the production of static road noise maps. The algorithms in the
NoiseModelling module take advantage of the spatial analysis methods offered by the GIS
environment. The calculation of acoustic indicators from traffic simulation is carried out in threesteps:- Geographic data, from the open Open Street Map (OSM) database, is imported into OrbisGis.
- The trajectories provided by Symuvia in xml format are imported in table form into OrbisGis,
via a specific function created for the purposes of this study.
- A set of SQL queries allows acoustic calculations.
The Noisemodelling computation core, called by these SQL queries, dissociates the estimation ofnoise emissions and the evaluation of noise propagation from sources to receivers. Both the calculationof emissions and sound propagation follow the CNOSSOS methods. Note that the Imagine method ispreferred to CNOSSOS for the calculation of the acceleration correction because CNOSSOS, intended
to estimate vehicle flow emissions, relies on a distance at intersections correction, which is lessappropriate for the dynamic modelling framework.
2.4 Case study and parameters
The case study consists of assessing the acoustic impact of road traffic on an urban network of
about 10 km² located in the Lyon conurbation, covering the 3rd and 6th districts of Lyon and part ofVilleurbanne (see Figure 2).

Figure 2 – Network and position of noise receivers
The implemented scenario simulates 3 hours of road traffic during the morning rush hour. Threelevels of travel demand are considered, ranging from a P1 period when the level of travel demand islow, to a P3 period when travel demand is almost doubled (morning rush hour). The assignment model
was validated to reproduce the flows measured on site during the period of interest in the Magnum
project 2.
2

https://magnum-erc.weebly.com/

893

Figure 3 – Demand levels
Three types of vehicles are represented in the traffic simulation: light vehicles (97% of the vehicles
generated on the network), heavy vehicles (2.5% of the vehicles generated on the network) and buses(0.5% of the vehicles generated on the network, depending on the bus network and therefore notaffected by the assignment processes).
From an acoustic point of view, heavy vehicles and buses are considered as belonging to category 3of CNOSSOS. Motorized two-wheelers are not included in the simulation.
Three 15-minute periods are used to perform the acoustic calculations: P 1[07:00 - 07:15], P 2[07:45- 08:00] and P 3 [08:45 - 09:00]. Thereafter, the variables relating to the periods Pi will be accompaniedby the index i.
Table 1 provides a summary of the parameters that were used for the acoustic calculation for thisstudy. 100 receivers are randomly placed on the network. Noise indicators are the L Aeq, LA10, L A50 and
LA90 .
Table 1 – Parameter values for sound propagation
Parameters

Configuration

Maximal order of reflexion

2

Maximal order of diffraction

1

Maximal distance source-receiver

500m

Building height

10m

Receivers height

1.5m

Ground absorption coefficient

G=0

Walls absorption coefficient

G=0.23

Spatial resolution

D=20m

3. RESULTS
3.1 Period P1
Period P 1 corresponds to the time range [07:00 - 07:15], during which the flows are already highenough, especially on the structuring axes. Traffic flows relatively smoothly, with average speeds
above 30 km/h over a large part of the network. The noise environment is highly correlated to thestructure of the road network, with very high noise levels along the ring road. The background noise,materialized by the LA90,P1, is also very high, the L A90,P1 also exceeding 70 dB(A). This is due to the
combined effect of the continuous flow of vehicles and their high sp eed (average speeds above 70km/h). The secondary network of small streets is characterized by very low flows, often below 200
veh/h, resulting in relatively low noise levels, with L Aeq,P1 of about 50dB(A) and L A90,P1 between 40894

and 45 dB(A).

a)b)

Figure 4 – Period P1: map of traffic and acoustic indicators

3.2

Period P2

The P2 period, which corresponds to the time slot [07:45 - 08:00], is marked by an increase in travel
demand, which is logically reflected in an increase of the flow rates on the network. This increase is
not uniformly spread over the network: some secondary roads see their number of vehicles doubled oreven tripled, while traffic flows remain stable or even decrease on some more travelled roads . This isdue to a decrease in average speeds, and the resulting route modifications. As a result, the increase innoise levels on the network is itself not uniformly distributed over the network: points near streets
where flows have increased significantly see their noise levels increas e significantly.

b)a)Figure 5 – Period P2: map of traffic and acoustic indicators relatively to period P1

3.3

Period P3

Period P3 corresponds to the time slot [08:45 - 09:00], marked by the peak of travel demand. The
increase in flow rates that was visible during P 2 is accentuated during P 3 , and extended over a larger
part of the network. Noise levels relative to P 1 increase together, with an increase in L Aeq of more than
3 dB(A) or even 5 dB(A) observed in some areas, particularly to the west of the network where theincrease in flow rates is very pronounced. The increase in noise levels on the ring road is contained895below 1.5 dB(A), as well as during P 2 .
The spatial distribution of the increase in levels remains uneven, with the western part of the
network being significantly more affected than the eastern part. The map also shows a distinction in
the increase in noise levels between quiet and noisy neighbourhoods. This point is discussed in moredetail in the following section.

a)b)

Rue Juliette

Cours Zola

Récamier

Figure 6 – Period P3: map of traffic and acoustic indicators relatively to period P1

3.4 Detailed analysis of noise results
The previous section pointed out that the increase in noise levels was not homogeneous over the
network. A closer look into the noise increase shows that for points where L Aeq,P1 > 70 dB(A), theincrease in L Aeq is smaller than 1 dB(A), while for the points where L Aeq,P1 is between 65 and 70 dB(A),this increase reaches 3 dB(A). In addition, background noise increases more than high levels, with L A90
increases well above L A10 increases. Thus, the increase in the number of vehicles on the network tends
to compress noise dynamics, increasing background noise more than already high levels. This is due tothe rarefaction in the simulation time steps when there are few vehicles in the vicinity of receivers.
The built modeling chain allows the fine analysis of this dynamic, si nce it gives access to theevolution of the instantaneous levels L Aeq,1s . An example of a more detailed analysis is given forreceiver n°22.

Figure 7 – Sound environment at receiver n°22.
Figure 7 shows the evolution of the L Aeq,1s for each of the three periods. The time series for period896

P1 shows the characteristic evolution of noise levels in streets where road traffic is clocked by traffic
lights, marked by a regular alternation between high and lower noise levels .
The quiet periods can be characterized by the indicator MI55, which represents the ratio of time
during which noise levels exceed 55dB(A): it is MI55 = 0.78 (22% of the time below 55 dB(A)) for theperiod P 1 . The increase in flows both in the street and the neighbouring streets result in a rarefaction of
the periods of calm that were renewed at the scale of the traffic cycle during the P 1 period: the time
during which the L Aeq,1s is below 55 dB(A) falls to 13% for P 2 , then to 9% for P 3 .

4. CONCLUSIONS
A dynamic modelling chain, coupling the Symuvia dynamic traffic model (LICIT, Ifsttar / ENTPE)
and the Noisemodelling noise level prediction tool (UMRAE, Ifsttar / Cerema), is developed in thispaper to assess the impact of road traffic on noise environments at the scale o f an urban traffic networkof about 10 km² located in Lyon/Villeurbanne. The traffic model is sensitive to dynamic assignment
phenomena depending on traffic conditions, and reproduces the kinematics of the vehicles present on
the network. The acoustic model estimates the instantaneous sound power levels as a function ofvehicle kinematics, then the noise levels in a receiver map after calculating the noise propagation as afunction of data on the building network.
The simulation, which reproduces 3 hours of a morning rush hour scenario, highlights the increasein noise levels associated with the increase in travel demands. It also highlights that the increase innoise levels: i) is not uniformly distributed over the network and has a greater impact on street s wheretraffic is reported, with noise levels increasing only slightly, for example, on the ring road due to alower increase in the number of vehicles compared to other streets, ii) has a greater impact on
background noise (and in particular the L A90 ), making quiet periods on the network rare, which is
explained by a higher vehicle density. A more detailed analysis is possible locally through the analysisof the evolution of the L Aeq,1s and the study of specific acoustic indicators (here the distribution of
LAeq,1s and in particular the ratio of time when the L Aeq,1s exceeds the 55 dB(A) threshold).
However, some improvements to the model are desirable, and will be the subject of research in thenear future, such as the estimation of emergence indicators as long as perceptual assessments. Then the
proposed model will make it possible to assess the acoustic impact, currently unknown, of road traffic
management strategies on the scale of an urban network: policies for spreading demand, speed limits
on a portion of the network, banning certain vehicles (motorcycles, old vehicles, etc.) in aneighbourhood, etc.

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