交通输入数据质量对道路噪声估计的影响使用CNOSSOS-EU方法
Elena Ascari1, Cătălin Andrei Neagoe2,3, Mauro Cerchiai1
1Institute of Chemical and Physical Processes of National Research Council, 56123 Pisa, Italy.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
在评估用于道路噪音映射的交通数据时,这项研究发现雷达计数器和人工智能摄像头提供可靠的输入. 然而,所有方法都显示出交通损失,导致低估的噪音水平,特别是在短时间间隔.
科学领域:
- 环境科学 环境科学
- 声学 声学 在声学上.
- 运输工程 运输工程
背景情况:
- 对于环境和城市规划至关重要,可靠的道路噪音映射在很大程度上取决于CNOSSOS-EU框架内的准确的交通输入数据.
- 欧洲各地异质的交通数据来源和测量实践带来不确定性,影响噪声估计的准确性和跨区域的可比性.
研究的目的:
- 评估三种不同的交通数据收集方法的性能:微波雷达计数器,基于AI的摄像头和Google API衍生流.
- 通过CNOSSOS-EU框架评估这些数据源对道路噪声模拟准确性的影响.
- 为道路噪声评估和战略绘制提供有关选择适当交通数据收集方法的指导.
主要方法:
- 在意大利和罗马尼亚的三个测试站点对雷达计数器,人工智能摄像头和谷歌API的交通流量和车辆类别数据进行比较分析.
- 使用CNOSSOS-EU动力模型与收集的交通数据进行道路噪声模拟,与现场噪声测量进行比较.
- 开发一种二次分析方法,将CNOSSOS-EU模型与声传播软件相结合,用于短期噪声水平估计.
主要成果:
- 微波雷达计数器和人工智能摄像头提供可靠的交通输入,用于聚合的白天/晚上/夜间噪声指标.
- 人工智能摄像头可能会在高流量情况下高估计计数,而雷达计数器可以在复杂的交通场景中错过流量.
- 谷歌API衍生流需要仔细校准,在每小时150辆以上的车辆中表现最好.
- 所有测试方法都显示出流量损失,导致模拟噪音水平的系统低估,特别明显在短期分析中.
结论:
- 雷达计数器和人工智能摄像头适用于战略道路噪音映射,但必须考虑它们的局限性.
- 谷歌API数据可以很有用,但需要特定的条件和校准才能准确的噪音建模.
- 交通数据收集方法显著影响噪声绘图的准确性,特别是在短期评估中;了解数据丢失对于可靠的道路噪声评估至关重要.
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