# EAgLE: Equivalent Acoustic Level Estimator Proposal

## Abstract

**:**

_{eq}, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured L

_{eq}and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators.

## 1. Introduction

_{den}evaluation), the need of a fast and effective model is more important than having an extreme precision (for instance lower than 1 dBA).

## 2. Materials and Methods

- Main function: it performs calculations needed to count the vehicles and separate the categories. It is also used to estimate the speed of each vehicle;
- Blob auxiliary function: it initializes the parameters of the blob, determining the bounding box and diagonal dimensions. In addition, it includes the “predictNextPosition” function described below;
- Header function: the blob class is defined here, applying the parameters defined in the “blob” function to each continuous mass detected in the frame.

_{m}is the average speed of the flow of the m-th category of vehicles, L

_{w,R,i,m}is the rolling noise, and L

_{w,P,i,m}is the propulsion noise, given by:

_{ref}being the reference speed (70 km/h), A and B table coefficients, and ΔL

_{w}the correction terms. Of course, other emission models can be easily implemented, according to the needs and the country of application of the EAgLE system.

_{w}is obtained for each vehicle, the instantaneous sound pressure level at the receiver L

_{p}(t) can be estimated using the pointlike source propagation formula, and the single event Sound Exposure Level (SEL) of each pass-by, i.e., the amount of acoustic energy of each transit “compressed” in 1 s, at the fixed receiver, is calculated:

_{0}= 1 s, t

_{1}, and t

_{2}, respectively, are the beginning and the end of the transit. This step is fundamental in order to make all the transits comparable, since they have strong differences in terms of duration, according to the speed of the vehicles [24]. This procedure is done for each vehicle and for each category, in particular for light and heavy duty vehicles. Then, the overall SEL is calculated with a log sum for light and heavy vehicles. The continuous equivalent level L

_{eq}evaluated in the time range Δt is finally obtained with the following formula:

- To acquire the video from cameras;
- To run the counting and recognition algorithm (in real time or in post processing analysis);
- To remove fake counts and adjust category recognition (only in offline analysis);
- To feed the noise level estimator with input data;
- To calculate noise emission levels (according to CNOSSOS-EU);
- To calculate the SEL of each vehicle;
- To calculate the overall SEL for light and heavy duty vehicles’ categories;
- To estimate the L
_{eq}on the required time basis (it should coincide with the video duration).

_{eq}can be tuned according to the needs of the case study. For instance, for urban planning purposes, in urban areas with specific limits, the L

_{den}(i.e., equivalent level evaluated on the day, evening, and night periods, with penalties for evening and night) can be calculated by running the algorithm on the video recordings of one year. Several other applications are possible, changing and tuning the parameters of the EAgLE methodology, depending on the aim of the investigation and on the case study.

#### Preliminary Application on a Case Study on an Italian Highway: Case Study Description

_{pA,F}, L

_{eq,A}, percentile levels, acoustic spectrum in third of octaves, etc., and the video of the vehicles passing-by have been recorded in parallel. Temperature was approximately in the range 11 °C–14 °C and wind speed was below 5 m/s on average. Furthermore, to protect the sound level meter from sudden wind peaks, the wind cover was used (see Figure 2d). The flow was running almost freely, with little variations of speed. The average number of vehicles flowing in 15 minutes is 1091 vehicles, with a percentage of heavy vehicles of about 15% in both the measurements. Details about the manual counts performed on the videos are reported in Table 1.

## 3. Preliminary Results

_{eq}on the 15 minutes time range, the levels predicted with some predictive statistical models, the levels predicted with CNOSSOS-EU model, and the L

_{eq}simulated with the EAgLE technique, are resumed in Table 4. The predictive models selected for the comparison are a fully statistical and simple model, i.e., the Burgess model [25], that includes just the traffic flow, the percentage of heavy vehicles, and the distance between source and receiver, and a “semi-dynamical” model, i.e., CNOSSOS-EU, that, in addition to the previous inputs, includes the mean speed of the flow and some correction factors, such as road gradient, temperature, etc.

_{eq}, while the models that consider the speed of the flow (as a mean value, such as CNOSSOS, or for the single vehicle, such as EAgLE) give a much better estimation of the noise levels.

## 4. Discussion

_{eq}. Furthermore, it should be underlined that at the moment the methodology presents some limitations and shortcomings.

_{den}estimation can be performed, using real traffic data, instead of simulating ideal conditions in noise predictive software. This could help local policy makers and infrastructure managers in finding the critical points of their networks and, if needed, in committing to implement further investigations, based on standard noise level measurements or other tools.

## 5. Conclusions

## Supplementary Materials

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Image analysis on the video frame. (

**a**) Moving object are bounded in a yellow box and a red dot (centroid) is applied. Green and white line are used for counting and speed estimation; (

**b**) blob detection after background subtraction.

**Figure 2.**Measurement location: (

**a**) Position of Baronissi (red mark), in the Campania region (courtesy of Google Earth©); (

**b**) 3D aerial view of the bridge from Google Earth©; (

**c**) lateral view of the bridge from Google Street View©; (

**d**) picture of the instruments during the measurement collection.

**Figure 3.**Speeds distributions for light (

**a**) and heavy vehicles (

**b**) summing the speeds estimated in both the video cuts.

Measurement ID | Starting Time [hh:mm:ss] | Light Vehicles Flow [veh/15 min] | Heavy Vehicles Flow [veh/15 min] | Percentage of Heavy Vehicles [%] |
---|---|---|---|---|

1 | 12:52:37 | 930 | 168 | 15.3 |

2 | 13:16:14 | 917 | 167 | 15.4 |

Period | |||||
---|---|---|---|---|---|

Period 1 [mm:ss] From–to | Period 2 [mm:ss] From–to | Period 3 [mm:ss] From–to | Period 4 [mm:ss] From–to | Period 5 [mm:ss] From–to | |

Video 1 cut | 00:00–01:02 | 03:40–04:41 | 06:03–07:03 | 10:30–11:30 | 13:55–15:00 |

Video 2 cut | 00:00–01:18 | 02:38–03:34 | 06:29–07:40 | 10:48–11:51 | 14:08–15:02 |

**Table 3.**Results of the manual and Equivalent Acoustic Level Estimator (EAgLE) counting and recognition, after the post processing of the video, in the two video cuts.

Manual counts | EAgLE Counts | Error Percentage | ||||
---|---|---|---|---|---|---|

Light Vehicles [counts] | Heavy Vehicles [counts] | Light Vehicles [counts] | Heavy Vehicles [counts] | Light Vehicles [%] | Heavy Vehicles [%] | |

Video 1 cut (308 s) | 334 | 52 | 342 | 55 | +2% | +6% |

Video 2 cut (322 s) | 349 | 64 | 355 | 67 | +2% | +5% |

**Table 4.**Summary of measured L

_{eq}over 15 minutes compared with predictive model results and with the L

_{eq}simulated with the EAgLE methodology.

Measurement ID | Measured L_{eq} [dBA] | L_{eq} Burgess [dBA] | L_{eq} Cnossos [dBA] | L_{eq} EAgLE [dBA] |
---|---|---|---|---|

1 | 75.9 | 77.9 | 76.2 | 76.0 |

2 | 75.9 | 77.9 | 76.1 | 75.9 |

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Guarnaccia, C.
EAgLE: Equivalent Acoustic Level Estimator Proposal. *Sensors* **2020**, *20*, 701.
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**AMA Style**

Guarnaccia C.
EAgLE: Equivalent Acoustic Level Estimator Proposal. *Sensors*. 2020; 20(3):701.
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**Chicago/Turabian Style**

Guarnaccia, Claudio.
2020. "EAgLE: Equivalent Acoustic Level Estimator Proposal" *Sensors* 20, no. 3: 701.
https://doi.org/10.3390/s20030701