• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Advanced Transportation >Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China
【24h】

Random Forests-Based Operational Status Perception Model in Extra-Long Highway Tunnels with Longitudinal Ventilation: A Case Study in China

机译：纵向通风的超长公路隧道基于森林的随机作业状态感知模型：以中国为例

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

An increasing number of extra-long highway tunnels have been built and put into operation around the world, but the quantified segmentation criteria for evaluating the in-tunnel operational status have not yet been enacted up till the present moment. Meanwhile, ventilation facilities could not satisfy the dynamic requirements of fresh air demand under fast spatial-temporal variation of traffic conditions and operating environment. In this study, the operational data collected from an extra-long highway tunnel were deeply analyzed using big data technology. By combining traffic flow and environmental monitoring data, a data-driven perception model based on the Random Forests was structured. The prediction results show that the proposed model provides better performances as compared to contrast models, indicating it had better ability to adapt to the dynamic changes of in-tunnel operational status while realizing accurate prediction. The designed intelligent control strategies of ventilation facilities and traffic operation applying for different operational status would provide a theoretical basis and data support for lifting the level of intelligent control as well as promoting energy saving and consumption reducing in extra-long highway tunnels.
机译：在世界范围内，越来越多的超长公路隧道已经建成并投入运营，但是到目前为止，尚未制定用于评估隧道内运营状态的量化分段标准。同时，在交通条件和工作环境随时间快速变化的情况下，通风设施不能满足新鲜空气的动态需求。在这项研究中，使用大数据技术对从特长公路隧道收集的运营数据进行了深入分析。通过结合交通流量和环境监测数据，构建了基于随机森林的数据驱动感知模型。预测结果表明，与对比模型相比，该模型具有更好的性能，表明在实现准确预测的同时，具有更好的适应隧道内运行状态动态变化的能力。所设计的针对不同运行状态的通风设施和交通运营智能控制策略，将为提高超长公路隧道的智能控制水平，促进节能降耗提供理论依据和数据支持。

著录项

  • 来源
    《Journal of Advanced Transportation》 |2018年第4期|5056284.1-5056284.10|共10页
  • 作者

    Qian Chao; Chen Jianxun; Luo Yanbin; Li Shuguang;

  • 作者单位

    Changan Univ, Sch Elect & Control Engn, Xian 710064, Shaanxi, Peoples R China;

    Changan Univ, Sch Highway, Xian 710064, Shaanxi, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 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