...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning
【24h】

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

机译：使用移动测量和机器学习预测细空间尺度交通噪声

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R~2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R~2 = 0.96,RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.
机译：环境噪声与各种健康终点有关，包括心血管疾病，睡眠障碍，抑郁和心理社会应激。大多数人口噪声曝光来自车辆流量，这产生了使用传统固定网站测量技术难以表征的微量空间变化。为了解决这一挑战，我们收集了一次加权，相当的噪音（Laeq，DB）数据，每小时长脚踏旅程，加利福尼亚州的长滩，并培训了四台机器学习模型，线性回归，随机森林，极端梯度提升和神经网络，预测20米分辨率的噪声。模型的输入变量包括流量指标，道路网络功能，气象条件和土地使用类型。在所有机器学习模型中，极端梯度提升在验证测试中具有最佳结果（休留 - 一排列R〜2 = 0.71，根均线误差（RMSE）为4.54 dB; 5倍r〜2 = 0.96， RMSE为1.8 dB）。局部交通量是最重要的噪音预测因子;道路特征，土地使用和气象包括湿度，温度和风速也有所贡献。我们展示了一种新颖的，与机器学习方法耦合的新型移动噪声测量方法使得能够在混合使用城市区域的交通相关噪声中的小规模空间模式的高度准确预测。

著录项

  • 来源
    《Environmental Science & Technology》 |2020年第20期|12860-12869|共10页
  • 作者

    Xiaozhe Yin; Masoud Fallah-Shorshani; Rob McConnell; Scott Fruin; Meredith Franklin;

  • 作者单位

    Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California 90032 United States;

    Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California 90032 United States;

    Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California 90032 United States;

    Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California 90032 United States;

    Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California 90032 United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号