Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, Leq, 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 Leq 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.

中文翻译：

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EAgLE：等效声级估算器提案。

道路基础设施是智慧城市发展的关键点。无论如何，应仔细评估道路交通对环境的影响。声噪声是通过声级测量来监视的最重要问题之一。当无法进行大规模测量时，可以使用道路交通噪声预测模型（RTNM）。文献中存在的标准RTNM通常需要在输入中提供一些有关交通的信息，例如车辆流量，重型车辆的百分比，平均速度等。很多时候，缺少有关此大量输入的信息是应用程序的局限性大规模的预测模型。本文提出了一种基于视频处理和目标检测工具的易于在传感器概念中实现的新方法：等效声级估算器（EAgLE）。通过分析研究区域的视频图像来检测EAgLE的输入参数。一旦记录了车辆的数量，类型（轻型或重型车辆）和速度，便根据欧盟推荐的标准模型（CNOSSOS-EU）和声暴露水平（在接收器处估算每次运输的SEL）。最后，总结所有车辆的贡献，可以评估给定时间范围内的连续等效水平Leq。本文提出了对EAgLE技术的初步测试，该测试针对在意大利高速公路附近进行的两个样本测量。与测得的Leq一致并与其他RTNM相比，结果将显示出出色的性能。这些令人满意的结果，一旦通过较大的验证测试确认，它将为开发专用传感器，嵌入EAgLE模型开辟道路，并可能在智能城市和道路基础设施监控中带来有趣的应用。实际上，这些站点通常出于安全或罚款/收费的目的而配备（或可以配备）监控摄像机的网络，一旦对模型进行了正确的校准和验证，就可以在大型网络中使用噪声估计器。