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

Opportunistic in-vehicle noise measurements assess road surface
quality to improve noise mapping: preliminary results from theMobiSense project.
Luc DEKONINCK1; Wout VAN HAUWERMEIREN1; Joachim DAVID1; Karlo FILIPAN2; Toon DE
PESSEMIER1; Bert DE COENSEL1,2; Wout JOSEPH1; Luc MARTENS1; Dick BOTTELDOOREN1
1Ghent University, Belgium

2

ASAsense cvba, Belgium

ABSTRACTThe quality of road pavements affects noise emission caused by tire-road interactions. This in turn affects thehealth and well-being of residents near these roads. Road pavement quality degrades over time due to wear,accidents, and infrastructure works. These local features are usually not included in noise mapping due to thelack of high-quality information on pavements with enough spatial resolution.
The aim of Mobisense is to assess the quality of the road surface by performing opportunistic noise andvibration measurements inside vehicles that are on the road for other purposes than road quality measurement.
In the demonstrator phase of the project, 20 vehicles collect data while the drivers make their usual trips.
Measurements from all vehicles are combined using machine learning techniques. This removes engine noise,corrects for vehicle specific speed dependence, and finally determines a rolling noise proxy in third-octave
bands. This rolling noise correction includes the effect of pavement type as well as the effect of road surface
degradation. This local variation in road surface quality is included as a correction in the rolling noise
component of CNOSSOS and used to calculate a subset of the noise map for the Flemish region in Belgium.
Including road surface quality in this way changes noise maps locally over a range of 6 dBA.
Keywords: Noise mapping, Road surface, Big Data

1. INTRODUCTION
Noise mapping is standardized in the Environmental Noise Directive for reporting noise exposure
to the European community. The common calculation methodology is compiled in the CNOSSOS-EU
The noise mapping relies on various sets of external data: traffic data, speed limits, fleet composition
and road surfaces. With the increased introduction of electric a nd hybrid vehicles and due toincreasingly stringent EU noise emission regulations, engine noise is dropping considerably. Rollingnoise caused by tire-road interaction therefore will become the next focus in traffic noise mitigation[1]. The influence of the choice of pavement and the actual state of the road surface will become more
important. This is particularly the case for urban areas. In addition to noise mapping, EU member
countries are required to draft action plans to mitigate (road traffic) noise. Pavement choice and roadmaintenance are possible mitigation measures that should show up in the noise maps. Hence a
continuous monitoring methodology could be very advantageous. Moreover continuous monitoring
could be a tool for noise emission labeling of road surfaces [2, 3].

2. IN-VEHICLE NOISE MEASUREMENTS FOR ROAD QUALITY PREDICTION
2.1 Opportunistic sensing – the Mobisense projectIn this paper an opportunistic approach is proposed: equipping cars that are on the road for other1

luc.dekoninck@ugent.be7971

purposes with noise and vibration sensors to assess road quality. For this, sensor boxes equipped witha microphone, a tri-axial accelerometer and a GPS sensor are put in the trunk of the fleet of cars.
Using a 3G connection, the acquired data is transferred to the analysis server. As computation power,
data storage, and wireless data transfer are decreasing in cost, opportunistic sensing and big data ison the advent. In addition, a landscape of open source software frameworks (Pandas, TensorFlow,NumPy, SciPy, scikit-learn, etc.) provide the scientific community with the necessary tools to utilize
big data, machine learning and artificial intelligence algorithms.
Rolling noise not only depends on the type and maintenance state of the pavement, but also on thetire that is used to sample it. Each vehicle has its own set of tires and therefore their noise emission
will vary. Moreover, the interaction between road and tires causes differences in the dependence on
driving speed [4]. In addition to the source mechanisms, also the transfer function between the tire
and the interior microphone may be quite different between vehicles, due to differences in microphone
placement and differences in construction between vehicles.
To account for all the above, the measurement data collected by each vehicle is related to theaverage measurement over all travelled roads by removing the speed dependent mean value. Averaging
over many vehicles with slightly different characteristics eventually results in a correction per thirdoctave frequency bands in the range 350Hz till 2500Hz where pavement quality matters. Thisaveraging converges quickly showing insensitivity to vehicle sampling. More details on themethpodology can be found in [2, 3]
2.2 Mobisense as an alternative for road pavement type and CPX
Noise mapping can be considerably improved by accounting for the type of pavement that is usedon each road segment. However, pavement databases are hard to keep up to date and mostly notavailable for lower level roads. It will be shown how opportunistic sensing could be an alternative toincluding road surface type from existing databases.
The state of the road surface related to rolling noise production is assessed regularly in many
countries using close proximity (CPX) measurements [5]. The Mobisense approach can add value tothis method by including a representative sample of tires used in the fleet and avoiding bias due totire choice in the CPX method [8]. Moreover, it is not limited to the standardized speed of the CPX
method and provides valid data for the entire road network sampled at a speed that is appropriate forthis type of road. Furthermore, information is obtained at a reduced operational cost and with faster
updates.

3. APPLICATION IN REGIONAL NOISE MAPPING
3.1 The reference: MIRA noise map of Flanders
The reference map is a region-wide noise map calculated for the Flemish Environment Agency
environmental reporting (MIRA) [6]. The noise map provides Lden, Lday, Levening and Lnight for all mainand secondary roads in Flanders. The map does not include shielding by dwellings but includes
detailed information on noise emission (CNOSSOS-EU) and noise barriers [7]. Its main asset is thespatial accuracy of the lower order roads. Traffic data is available on a generalized network but byrouting the generalized links on a routable OSM streetmap layer ( www.openstreetmap.org), the traffic
is mapped to its exact physical location. The underlying grid of immission points lies on distances of
10, 20, 50 and 100 m from the roads and on a 100 by 100 grid outside the first 100 m buffer of theroad network. The maximum exposure near a road is by design the immission at 10 m distance fromthe center of the closest road.
A pavement dependent rolling correction is classically implemented based on a pavement databasefrom the Flemish road Agency (AWV, https://wegenenverkeer.be/). This dataset is incomplete and forthe secondary roads the pavement types are not available. Missing data is extrapolated stochastically.
The overall statistics on used pavements is valid, but locally there is no guarantee that the actual roadsurface is used.
The next three figures show (1) the original map (Lden,ref), including the road surface corrections
based on the pavement database, (2) the map without the road surface correction (Lden,no_road), (3) thedifference between both maps. For the purpose of this paper, only the main roads are included.

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Figure 1 – Lden noise map based on pavement database (Lden,ref)Figure 2 – Lden noise map without pavement corrections (Lden,no_road).
TO REPLACE

Figure 3 – Effect of pavement correction on Lden (Lden,ref - Lden,no_road)7973

3.2 Applying the Mobisense road surface correction
The noise emission model is based on CNOSSOS-EU. The noise emission is calculated as the sumof the engine noise and the rolling noise. The measured corrections are limited to the octave bands:
250, 500, 1000 Hz and 2000 Hz. At lower frequencies engine noise tends to dominate or the emission
contributes marginally to the overall A-weighted level. Rolling noise at higher frequencies may be
influenced too much by air pumping for the mobisense results to be valid, yet their contribution to Aweighted overall levels is also limited. A visual representation of the local road surface corrections(in 25 m segments) is shown in Figure 4. Most highways and a high percentage of main roads has
already been sampled and the opportunistic sampling is ongoing. The correction is shown for all
sampled roads but only the main roads are included in the mapping (see previous section).

Figure 4 – Mobisense corrections by road segment of 25 m
3.3 Calibration of the two procedures
The CNOSSOS emission functions are based on large samples of vehicle CPB’s. CNOSSOS noisemaps include the hypothesis that the local fleet matches the sample of vehicle used to determine theemission functions. In the rolling noise component, a reference road surface and corrections areavailable. The Mobisense procedure assumes a zero correction on rolling noise emission at each octave
band for the average road surface travelled by each car. Thus, a calibration should be included to relate
the average Flemish road to the standard CNOSSOS road. This calibration step has thus far not been
performed. With this calibration and a large sample of measurement cars, Mobisense should reflect
the average fleet, including tire types and tire wear better than the CNOSSOS-EU rolling noisefunctions. On the other hand, the Flemish road Agency has supporting data that the standard roadsurface in Belgium results in lower rolling noise compared to the reference road surface in theCNOSSOS-EU method. Thus an offset between including the expected road performance and theactual road performance (from Mobisense) is expected.

4. Results: noise mapping with Mobisense road surface corrections
4.1 Mobisense road surface corrections.
Figure 5 presents the difference in L den due to the Mobisense road surface correction. In the yellowcolored areas the difference might also be zero as these roads may not have been sampled during thepilot phase. Red areas correspond to higher immission than expected by the calculation with thereference road surface, green areas represent lower immission than expected with the reference surface.
Figure 6 gives the same results zoomed to the Ghent area. Most of the main highways are sample dand show distinct patterns matching the local variations between quiet and noisy road surfaces.

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Figure 5 – Effect of Mobisense road surface corrections on Lden (Lden,mobi - Lden,no_road) ;red = noisier surface
than reference.

Figure 6 – Effect of Mobisense road surface corrections on Lden (Lden,mobi - Lden,no_road) – detail for Ghent
4.2 Mobisense road corrections compared to pavement type corrections.
The magnitude and alignment of the respective corrections is visualized in Figure 7 as a 2Dhistogram of cell-by-cell comparison. Only cells with L den larger than 60 dBA are selected to limit theamount of data points and to remove the cells affected by the local roads only. The cells with a
Mobisense correction less than 0.2 dB are removed to limit the dataset to cells that are affected by theMobisense sensing campaign.
The 2D histogram peaks at slightly negative values for both the calculation based on assumed
reduction in rolling noise due to pavement choice and the calculation based on measured reduction ofrolling noise. However, it can also be seen that for a large number of locations Lden,mobi - Lden,no_road islarger than Lden,ref - Lden,no_road which indicates that rolling noise is higher than theoretically expectedon the basis of the pavement choice. On the other hand, there are surfaces where noise levels basedon measured characteristics are lower than those based on average expectations for the selectedpavements. Part of this discrepancy may be due to the timeliness of information in the pavement
database.
In Figure 8 the distributions of the cell-based comparison of the three noise maps are visualized.
The road surface corrections retrieved from the CNOSSOS-EU emission functions and the pavement
database, Lden,ref - Lden,no_road shows a narrower distribution compared to the measured Mobisense roadsurface corrections (IQR 1.65 vs IQR 2.13). The distribution of the differences between bothcorrections is even larger (IQR 2.5 dB). The offset in the distribution of 0.96 dB (median) illustrates
the unknown calibration factor between the two methods. The restriction to cells with L den > 60 dBA
induces a spatial bias towards roads with higher traffic intensity and the resulting offset in the7975

Lden,mobi - Lden,no_road

distribution of Lden,mobi - Lden,no_road illustrates that low noise road pavements are more common onthese high intensity roads.

Lden,ref - Lden,no_road

Figure 7 – Cell-wise comparison between Lden,mobi - Lden,no_road and Lden,ref - Lden,no_road (2D histogram)

Figure 8 – Distributions of cell- based comparison between the noise maps.
Figure 9 presents the comparison between the L den,ref and Lden,mobi maps. The offset is not adjusted.
Areas in red represent higher immission level than expected from the pavement type databases.

Figure 9 – Difference in effect on Lden between Mobisense and pavement database (Lden,mobi - Lden,ref)7976

5. Discussion
The Mobisense opportunistic measurement method reveals the spatial variability of the roadsurface quality and its effect on rolling noise. It gives highly complementary information on the classof the road pavement with respect to rolling noise generation. Correlation with noise maps obtainedby tuning the CNOSSOS model to road surfaces as retrieved from a road pavement inventory are
rather low although the distribution of corrections peaks near slightly reduced immission for both
approaches. Part of the discrepancy may be due to the lack of timely information on road pavements.
But aging and damage due to intensive use, accidents, and local features such as bridges maycontribute. The latter is even more pronounced for lower level roads owned by local authorities.
A calibration between Mobisense and the CNOSSOS-EU emission functions for reference roadpavement will be necessary. Several options are under investigation. The strongest approach is toperform road-side measurements to align both methods. Third-party data (CPX and laser
measurements of local road quality) have been linked to these outcomes to perform additional external
validations.

6. Conclusions
The opportunistic sensing approach based on sensing noise and vibrations inside vehicles that oron the road for other purposes is very promising. The road surface corrections based on thesemeasurements are complementary to data that can be retrieved from road pavement databases andstandard corrections. Some road surfaces are under-performing compared to standard corrections, butothers result in lower rolling noise than expected. These results support the overall goal of themobisense methodology. The measurements can detect the deterioration of the road surfaces in a highspatial resolution. Overall, the Mobisense approach will add more value on the secondary road
network where the quality of the pavement databases is expected to be low. Intervention on roadquality can be guided by the opportunistic measurements and can become part of action plans. The
local impact of road deterioration on the annoyance assessments and its potential relevance in healthimpact studies requires further investigations.

ACKNOWLEDGEMENTS
This work was performed within the framework of the ICON project MobiSense (grant No.
HBC.2017.0155), supported by IMEC and Flanders Innovation & Entrepreneurship (Vlaio).

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