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

A NEW APPROACH USING BIG DATA TO IMPROVE ROAD TRAFFICNOISE MAPPING
Gaetano LICITRA1; Antonino MORO2; Luca TETI3; Alessandro DEL PIZZO4; Francesco BIANCO5
1

ARPAT – Environmental Protection Agency of Tuscany Region, Area Vasta Costa, Via Marradi 114, Livorno,Italy and CNR-IPCF, Via Moruzzi 1, 56124 Pisa, Italy
2

CNR-IPCF, Via Moruzzi 1, 56124 Pisa, Italy
34

IPOOL S.r.l., Pisa, Italy

University of Pisa, Physics Department, Pisa, Italy
5IPOOL S.r.l., Pisa, Italy

ABSTRACTThe present paper describes a new approach developed within the BEEP project (Big data for Environmentaland occupational EPidemiology) aimed to improve road traffic noise mapping in epidemiological studies.
The BEEP project, funded by the National Institute for Insurance against Accidents at Work (INAIL), aims atusing big data for the evaluation of negative health effects due to air and noise pollution on the Italian
population and the risk of occupational injuries in sub-populations of workers.
Noise maps provide noise emissions that are usually calculated by traffic flows measured or derived by amodel. The evaluation of traffic flows can determine significant uncertainty in noise estimate: accurate traffic
data can significantly improve the meaningfulness of noise models.
In this paper, a new method based on Google API and Big Data treatment was developed to estimate traffic
flow and to produce noise maps of Rome agglomeration.
Correlation between travel time and traffic flow considering road characteristics was found by street
clustering. Noise maps when obtained will be compared to those produced by conventional means toinvestigate if the use of big data could improve traffic estimates, in particular during the night period, whichis well known to be strongly related to health issues.
Keywords: noise mapping, big data, health effects

1. INTRODUCTION
The BEEP project, funded by the National Institute for Insurance against Accidents at Work(INAIL), aims at using big data for the evaluation of negative health effects due to air and noise
pollution on the Italian population and the risk of occupational injuries in sub-populations of workers.
Noise maps provide noise exposure levels that are usually calculated by traffic flows measured orderived by a model.
The evaluation of traffic flows can determine significant uncertainty in noise estimate: accurate
traffic data can significantly improve the meaningfulness of noise models.
The main objective of this paper was the development of a new approach within the BEEP project(Big data for Environmental and occupational EPidemiology) aimed to improve road traffic noise
mapping in epidemiological studies.
In particular, a new method based on Google API and Big Data treatment was performed to acquire1

gaetano.licitra@arpat.toscana.it
antonino.moro@unirc.it
3luca.teti@i-pool.it
4
alessandro.delpizzo@df.unipi.it
5
francesco.bianco@i-pool.it
25167

travel time information and to estimate traffic volumes using travel time functions and to produce
noise maps of Rome agglomeration.

2. BIG DATA TREATMENT AND ACQUISITION OF COLLABORATIVE TRAFFIC
DATA
2.1 Use of Big Data to improve noise mapping
As well known, correct traffic flow estimation contributes significantly to the final accuracy of anoise map (1,2).
Traffic models are often available only for rush hour and several approximations are made toprovide the input parameters required for noise mapping, as average traffic flows, average speed andvehicles categories for reference periods.
In addition, in order to improve the noise level assessment in cities, another very important aspectis the street classification (3), required for the choice of in situ measurement points needed for thecalibration of the used noise level model.
This point becomes critical in an agglomeration like Rome, the subject of this study, which presents
tens of thousands of road links and in which several small roads were not considered in the trafficmodel.
To deal with these problems, the BEEP project wants to improve results of noise mapping with thehelp of reliable big data information on traffic flow as data from social media (4).
2.2 Acquisition of collaborative traffic data
In this study, road link travel times were acquired by means of a API developed by Google. During
the experimental survey, described in a later section, the travel time acquisitions were madesimultaneously with in situ noise measurements and traffic flow counts.
The travel times were later used to estimate traffic volumes through appropriate link delay
functions.
Recently also other studies (5) have used collaborative traffic data for road noise mapping with adifferent final treatment.

3. TRAVEL TIME FUNCTIONS
3.1 Link congestion functions
In order to convert the extracted duration in traffic times in traffic volumes, the most commonlyused volume-delay functions were considered.
In these models the travel time t (or the speed u) on a road link is expressed as a function of traffic
volumes (6, 7, 8, 9, 10).
The main four models available in literature are summarised in Table 1.
The BPR (Bureau of Public Roads) function shows a simple mathematical form that has allowed its
wide use and application. But, it is important to remark that this model is particularly suitable for links
not subject to congestion conditions and does not take into account road characteristics as signalizedlinks.
The Akcelik function is suitable for modelling traffic conditions characterized by delays due tointersections and signals.

5168

Table 1 – Main volume delay functions
ModelBPR

Equation𝑢=(); 𝑡 =𝑡 ∗ 1+𝛼∗

References()

𝐽 𝑥1−𝑥Davidson𝑡 =𝑡 ∗ 1+Akcelik𝑡 = 𝑡 ∗ 1 + 0.25𝑟 (𝑥 − 1) + (𝑥 − 1) + 8𝐽

Conical𝑢=(6, 7, 8)(9, 10)𝑥𝑟

𝑢2 + 𝛽 (1 − 𝑥) + 𝛼 − 𝛽(1 − 𝑥) − 𝛼(6,10)(6)

Legendu0 = speed at free flow; t0 = travel time at free flow; q = flow rate; Q = capacity; q’ = flow rate on
the opposite direction of link; x = q/Q = degree of saturation; α, β, γ, JD = calibration parameters.

4.

ROAD-LINK CLUSTERING AND EXPERIMENTAL PLAN

4.1

Study area description and initial road-link clusteringAn experimental survey was designed within a study area located in the city of Rome and covering
an area of 144 Km2 . Figure 1 shows the road links inside the study area.

Figure 1 - Road links inside the study area
The road links were appropriately divided into homogeneous groups defined on the basis of thedaily traffic trend. In particular, a link clustering process was carried out considering the percentage

5169

trends with respect to the total daily traffic flow provided by traffic model for 7 periods as summarizedin Table 2.
This analysis led to the definition of three clusters, named C1, C2 and C3 containing respectively3665, 1194 and 6581 links. Figure 2 shows the result of the clustering process.

Figure 2 - Result of the clustering process

Table 2 – Periods

4.2Periods

Time range

112-7 a.m.

2

7-9 a.m.

3

9 a.m.-12 p.m.

412-2 p.m.

52-4 p.m.

64-8 p.m.

78 p.m.-12 a.m.

Experimental Plan

An experimental plan was designed to simultaneously collect in situ noise and traffic data andtravel times by Google API.
The experimental survey combined noise and traffic volumes (light, heavy and motorcycles)
measurements in 42 road links during day, evening and night periods. The 42 measure points wereselected on the basis of the clustering process.
In particular, road links of the three identified clusters and with different geometric characteristics(one-lane, two-lanes and three-lane roads in each traffic direction) were chosen.
At each measure point, four 30-min measurements were performed during two periods: 6-8 a.m.
and 20-23 p.m.

5170

5. First analysis of results
This section contains the first interesting analysis of the results in terms of the comparison of thedata extracted by Google API with the traffic data collected in situ.
5.1 Critical issues
Before proceeding with the estimate of the traffic volumes by the travel times of Google API, it isnecessary to highlight a series of possible critical issues occurred from the preliminary analysis andreported in the following points:- for 18 roads out of 42, the travel times taken are characterized by constant or too little variablevalues in the examined periods;- in some cases the travel time values appear to be too high in relation to the reduced number ofcounted vehicles;- in one case the extracted travel time is lower than that corresponding to the free flow condition.
The road links mainly affected by these problems were those with one-lane and characterized by
low flow rate. For the roads affected by these issues, it was not possible in this first phase of analysis
to obtain estimates of traffic volumes.
5.2 Comparison between estimated and counted traffic flows
The best results have been obtained in the case of two-lane roads in each direction characterized byflows exceeding 200 PCE (Passenger Car Equivalent) per hour. The following figure 3 seems to show
a good correlation between travel time per unit distance extracted by Google API and in situ counted
traffic volumes (expressed as PCE per hour).

Figure 3 – Acquired travel times vs PCE per hour
In the analysis carried out, since the investigated road links did not present traffic light systems andwere not affected by congestion conditions, we used the model BPR to estimate the traffic volumes.
The figure 4 shows the comparison between counted traffic flows and estimated traffic flows using theacquired travel time by Google API for two lanes roads in each direction.

5171

Figure 4 - Comparison between estimated and counted traffic flows
In the case of two lanes roads in each direction, for flow rates greater than 200 PCE per hour but
less than capacity, the results seem to be very interesting.

6. CONCLUSIONSIn this study, a new method based on Google API and Big Data treatment was performed to acquire
travel time information and to estimate traffic volumes using travel time functions and to produce
noise maps of Rome agglomeration.
For this purpose, an experimental plan was designed to simultaneously collect in situ noise andtraffic data and travel times of Google API.
The first analysis showed a series of possible critical issues mainly for the road links with one-lane
and characterized by low flow rate.
Otherwise, encouraging results have been obtained in the case of two-lane roads in each direction
characterized by flows exceeding 200 PCE (Passenger Car Equivalent) per hour but not affected bycongestion conditions. Under these conditions, it was possible to estimate traffic volumes using theextracted travel times.
In the next analysis, noise maps when obtained will be compared to those produced by conventional
means to investigate if the use of big data could improve traffic estimates, in particular during the nightperiod, which is well known to be strongly related to health issues.
Big Data can be a great help to improve the accuracy of the noise maps, but future research isneeded to define well conditions and their field of application.

ACKNOWLEDGEMENTS
This work, part of the BEEP (Big data for Environmental and occupational EPidemiology) project,
has received funding from INAIL (National Institute for Insurance against Accidents at Work),
contract ID 04/2016, in the framework of the BRiC 2016-2018 (Bando Ricerche in Collaborazione)
calls.

REFERENCES
1. Licitra G, Memoli G. Limits and advantages of Good Practice Guide to Noise Mapping. In: J. Acoust.
Soc. Am. 123(5):3033. (2008).
2. Fogola J, Galdi A, Albani L, Masera S. Gestione dell’inquinamento acustico e mappatura acustica
strategica degli agglomerati urbani. In: Proceedings of Workshop AIA “La mappatura acustica strategica
degli agglomerati urbani nel secondo ciclo di attuazione della Direttiva Europea 2002/49/CE”, Pisa
2011.
3. Gómez Escobar V, Pérez CJ. An objective method of street classification for noise studies. In: J. Applied
Acoustics 141 (2018), p. 162-168.

5172

4. Licitra G, Fogola J, Ascari E. The use of Big Data for improving noise mapping in epidemiologicalstudies. In: 25th International Congress on Sound and Vibration 2018, ICSV 2018, Hiroshima.
5. Ladino AV, Duque C, Castrillon SA, Durango CE, Murillo DMG, Osorio AFM, Carranza JM.
Collaborative traffic data for road noise mapping. In: Inter-Noise 2018, 26-29 August Chicago, Illinois.
6. Mtoi ET, Moses R. Calibration and Evaluation of Link Congestion Functions: Applying Intrinsic
Sensitivity of Link Speed as a Practical Consideration to Heterogeneous Facility Types within Urban
Network. In: Journal of Transportation Technologies, 2014, 4, p. 141-149.
7. Manzo S, Nielsen OA, Prato CG. In: The Effects of Uncertainty in Speed-Flow Curve Parameters on aLarge-Scale Model. Transportation Research Record, 2014, 1, 30-37. https://doi.org/10.3141/2429-04.
8. Nielsen OA, Jørgensen, RM. Estimation of speed–flow and flow–density relations on the motorway
network in the greater Copenhagen region. In: IET Intell. Transp. Syst., 2008, Vol. 2, No. 2, pp. 120–131 doi: 10.1049/iet-its:20070024
9. Davidson, KB. A flow–travel time relationship for use in transportation planning. In: Proc. 3rd ARRB
Conf. 3 1966, pp. 183-194.
10. Akçelik R (1991). Travel time functions for transport planning purposes: Davidson's function, its
time-dependent form and an alternative travel time function. Australian Road Research 21, 1991, pp.
49–59.

5173