PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

An Australian case study on the estimation of heavy vehicle noiseemission on grade
Jeffrey Peng1; Daipei Liu1; Jeffrey Parnell1,2; Nicole Kessissoglou1
1

School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, Australia
2

NSW Department of Planning and Environment, Australia

ABSTRACT
Heavy vehicles are considered the primary determinant of night-time noise disturbance, particularly along
principal freight routes. To capture the dynamic influence of heavy vehicles associated with variation in
speed and road grade on noise emission, heavy vehicle kinematic variables need to be incorporated within aroad traffic noise emission model. These kinematic variables in turn assist with accurate estimation of engine
noise and rolling noise. An existing prediction method that considers the driving speed profiles of articulated
trucks is the American FHWA TNM road traffic noise model. However, it can only consider a fixed set of
speed profiles based on a single heavy vehicle power-to-weight ratio. As such, the model is limited and does
not accurately represent the longer and heavier vehicle combinations that dominate the Australian freight
haul fleet. In this work, a road traffic noise prediction model which includes the equation of motion for atypical Australian heavy vehicle operating on grade is presented. A case study based on a principal freightroute in New South Wales, Australia, is presented to illustrate the predicted variations in engine noise androlling noise throughout the heavy vehicle’s journey.
Keywords: Heavy vehicle dynamics, traffic noise prediction, acceleration, speed1. INTRODUCTION
Road traffic noise is one of the most prevalent sources of environmental noise resulting inannoyance as well as health concerns such as hypertension and sleep deprivation (1). Specifically,noise emitted by heavy vehicles has been identified as adversely impacting on those living along ornear freight routes (2). The noise problem is further compounded by the use of longer and heavier
truck-trailer combinations with a greater number of axles to increase freight productivity (3). The twomain sources of noise emission of a heavy vehicle are engine noise which is associated with engine
load, and rolling noise generated at the interface between the vehicle tyres and road surface. Variationin road grade and vehicle speed affects the contributions of to the overall noise emission of a heavyvehicle (4,5). Rolling noise is most prominent in high-speed environment while engine noise becomesincreasingly dominant as speed reduces. The accurate assessment of road traffic noise requires that thecontributions of both heavy vehicle engine noise and rolling noise be calculated for realistic operating
scenarios.
Existing research recognises the need for accurate estimation of kinematic variables to capture thevariation in the contributions of rolling noise and engine noise (6). The consideration of heavy vehicle
dynamics within principal road traffic noise emission models remains insubstantial. European traffic
noise models default to maximum legal speed limits (7). The American FHWA-TNM is the only
government approved method that considers heavy vehicle dynamics, whereby speed profiles of astandard 5-axle single-trailer truck are incorporated into the model to enable more accurate predictionof heavy vehicle noise levels along a route featuring different road grades and speed profile zones (6).
However, the approach is based on curve-fitting of speed profile data and the range of validity is
restricted to one particular truck-trailer configuration and power-to-weight ratio. A fixed set of heavy
vehicle speed profiles is insufficient in the Australian context because the predominant long-haulfreight vehicle has been trending towards longer and heavier vehicle combinations since the 1990s(3,7).
1

jyt.peng@gmail.com

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In this work, the Nordic and American road traffic noise prediction models are combined with asimple analytical model to yield the equation of motion for a heavy vehicle on grade. The Nordic
model is used to replace rolling noise emission featured in the American model as it includes theability to vary heavy vehicle rolling noise emission based on the axle configuration. The proposed
analytical model considers rolling resistance, grade resistance, aerodynamic drag force, as well as
tractive force delivered by the engine. The equation of motion provides a relationship between thedistance travelled by the heavy vehicle as a function of speed and is applied to a 5 km long section of
the Princes Motorway in New South Wales, Australia. In this case study, a 6.4 kW/t 9-axle
double-trailer truck is used to demonstrate the transition in dominance from rolling noise to engine
noise as a heavy vehicle ascends road grades of up to 8.6%.

2. ROAD TRAFFIC NOISE EMISSION MODEL
The noise emission of a stream of heavy vehicles is customarily characterised by a line sourcecomprising a series of incoherent point sources (6). In this present work, heavy vehicle noise emission
represented at each point source comprises the rolling noise algorithm from Nord2005 and full-throttle
engine noise from FHWA-TNM (4,5):
𝐿,= 𝐴 + 𝐵 log
𝐿,𝑣 ,+ 10log
70𝑁
4= 111.6(1)(2)

In Eq. (1), the sound power at the tyre/pavement interface is given as a function of the number of axles
𝑁 and mean vehicle speed 𝑣 , along each road segment represented by the point source. For rollingnoise, the regression coefficients (𝐴 and 𝐵 ) at each one-third octave band centre frequency aretaken from Ref. (4) for Swedish, Norwegian and Finnish conditions. Furthermore, in accordance withRef. (4), rolling noise is increased arithmetically by 3 dB per doubling of axles with reference to a4-axle truck to account for the increase in road/tyre contact noise for a 9-axle truck. In Eq. (2), engine
sound power level under full-throttle is taken as 111.6 dB(A) irrespective of vehicle speed and grade.
The expression of engine noise from the FHWA-TNM model ignores the small perturbations in engine
rotational speed from the gear-changing sequence. As seen in Figure 1, when the 9-axle truck isoperating under full-throttle condition, the contributions of rolling noise and engine noise to theoverall noise emission depend solely on vehicle speed. At 70 km/h, rolling noise is equal to engine
noise under full-throttle. Engine noise becomes increasingly dominant as vehicle speed reduces.
Below 35 km/h, rolling noise becomes inconsequential as engine noise is shown to be 10 dB higher.

Figure 1 – Rolling noise and full-throttle engine noise as a function of vehicle speed3736

3. EQUATION OF MOTION FOR A HEAVY VEHICLE ON GRADE
To calculate the vehicle speed as a function of distance travelled, the equation of motion for a heavyvehicle on grade is initially defined as (7):𝐹 =𝑚 𝑎 =𝐹 −𝐹 −𝐹 −𝐹(3)

where 𝑎 is the acceleration and 𝑚 is the effective mass of the vehicle, corresponding to the sum of
the vehicle’s static mass and the equivalent dynamic mass to account for the loss of tractive force dueto the inertia of the engine and driveline components. The significant forces correspond to theretarding forces associated with aerodynamic drag 𝐹 , rolling resistance 𝐹 and grade resistance 𝐹 ,as well as the tractive force 𝐹 delivered by the engine under full-throttle to overcome the retarding
forces. According to Eq. (3), the vehicle will accelerate when tractive force is greater than the sum of
all resistance forces. Conversely, the vehicle will decelerate when the engine is not able to deliver
sufficient power to overcome all the resistances forces, in which case the heavy vehicle would slow
down even under full-throttle condition. Furthermore, on any given grade, the maximum sustainable
speed (also known as crawl speed) of a vehicle is reached when the tractive force is equal to sum of allresistance forces. Re-arranging Eq. (3) in terms of acceleration, the estimated crawl speed for a
specific heavy vehicle combination on any given grade is obtained under full-throttle by setting 𝑎 = 0.
Using the simple kinematic relation 𝑎𝑑𝑥 = 𝑣𝑑𝑣 for translational motion, entrance speed 𝑣 and exit
speed 𝑣can be computed by numerical integration at the respective ends of a given road segment.

4. VARIATION IN ROLLING NOISE AND ENGINE NOISE
Figure 2 shows the computed speed profile as a function of road grade and distance travelled, for a
6.4 kW/t 9-axle double-trailer truck traversing the Great Dividing Range at Mount Ousley in New
South Wales, Australia with an approach speed of 100 km/h. As shown in Figure 2, there are six
distinct changes in the road grade along this section of the Princes Motorway, ranging from 0% to 8.6%.
The speed profile shows that the 6.4 kW/t double-trailer truck operating under full-throttle is barely
maintaining its initial speed of 100 km/h on a relatively flat section of the road and begins to decelerate
rapidly as road grade reaches 3.3%. Figure 3 presents the variation in the contributions of rolling noiseand engine noise at each point source location. As seen in Figure 3, rolling noise is only observed to be
dominant during the first 1200 metres of the vehicle’s journey as it travels from the south. For theremainder of the vehicle’s journey through the adjacent residential suburb, engine noise is found toexceed rolling noise as the 9-axle double-trailer truck is unable to maintain its speed above 70 km/h.
On 8% grade, the crawl speed of double-trailer truck is 22.5 km/h, in which the full-throttle engine
noise is shown to be more than 15 dB above rolling noise. Immediately after the 8% grade, vehicle
speed (see Figure 2) and the contribution of rolling noise (see Figure 3) increases momentarily as thesignificantly lesser grade resistance on the 1.5% grade allows the double-trailer truck to accelerate
before another steeper section is encountered. Based on the above case study, it is concluded that aroad traffic noise prediction model that defaults to maximum legal speed limit would overestimate thecontribution of rolling noise. Furthermore, a road traffic noise prediction model that does not consider
vehicle dynamics would be unable to identify localised areas impacted by high levels of engine noise
when designing appropriate noise mitigation measures such as barriers.

5. CONCLUSIONSIn this work, a road traffic noise prediction model that considers heavy vehicle dynamics has been
applied to a 5 km section of the Princes Motorway in New South Wales, Australia, with road gradesvarying from 0% to 8.6%. Results show that a 6.4 kW/t 9-axle double-trailer truck is unable tomaintain its initial approach speed, with speed reducing from 100 km/h (along a relatively flat sectionof road) down to just over 20 km/h (when negotiating the steepest part of the ascent). Engine noise wasfound to dominate heavy vehicle noise emission over the majority of the heavy vehicle’s journey on
uphill grade, with the contribution of rolling noise reducing rapidly as speed decreases on grade. The
significance of this finding is the identification of appropriate noise mitigation measures depending on
the dominance of engine noise and rolling noise. Further work will consider more case studies toinvestigate heavy vehicle noise emission and the duration of the sound exposure at receiver locations
associated with heavy vehicle dynamics using the model proposed in this paper.

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Figure 2 – Variation in grade and speed as a function of distance travelled for a 9-axle heavy vehicle

Figure 3 – Variation in the contributions of rolling noise and engine noise (vehicle heading north)(aerial photography of Mount Ousley was sourced from the NSW Government)

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ACKNOWLEDGEMENTS
The lead author would like to acknowledge the financial support from the International
Commission for Acoustics, Acoustical Society of America and German Acoustical Society, for a 2019
ICA-ASA-DEGA Young Scientist Conference Attendance grant. Any opinions expressed are those ofthe authors and do not necessarily reflect those of the New South Wales State Government.

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