...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer Communications >An adaptive and Distributed Traffic Management System using Vehicular Ad-hoc Networks
【24h】

An adaptive and Distributed Traffic Management System using Vehicular Ad-hoc Networks

机译：使用车辆ad-hoc网络进行自适应和分布式流量管理系统

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

Traffic Management Systems become an important challenge for large cities due to the constant growth of vehicles. As the road mesh does not increase as well as the number of vehicles in the streets, technological solutions for the traffic congestion rise as alternative and easy-to-use applications. This work presents the ON-DEMAND: An adaptive and Distributed Traffic Management System using VANETS. The proposed solution is based on V2V communication and the local view of traffic congestion. During its displacement in a road, the vehicle monitors its traveled distance and the expected one considering a free-flow traffic condition. The difference between these measurements is used to classify a contention factor, i.e., the vehicle perception on the road traffic condition. Each vehicle uses the contention factor to classify the overall congestion level and this information is proactively disseminated to its vicinity considering an adaptive approach. In the case a vehicle does not have the necessary traffic information to estimate alternative routes, it executes a reactive traffic information knowledge discovery. The proposed solution is compared with three literature solutions, named DIVERT, PANDORA and s-NRR. Our results showed that ON-DEMAND presents better results regarding network and traffic congestion metrics.
机译：由于车辆不断增长，交通管理系统成为大城市的重要挑战。随着道路网的街道和街道的车辆数量不增加，交通拥堵的技术解决方案越来越易于​​使用的应用。这项工作介绍了按需：使用VAGET的自适应和分布式流量管理系统。该解决方案基于V2V通信和交通拥堵的局部视图。在道路上的位移期间，车辆监控其行驶距离和考虑自由流量交通状况的预期距离。这些测量值之间的差异用于对争用因子进行分类，即对道路交通状况的车辆感知。每辆车都使用争用因子来分类整体拥塞水平，并且在考虑自适应方法的情况下主动传播该信息。在车辆没有必要的交通信息来估计替代路由，它执行反应性交通信息知识发现。将所提出的解决方案与三种文献解决方案进行比较，名为DECERT，PANDORA和S-NRR。我们的结果表明，按需提出了有关网络和交通拥堵指标的更好结果。

著录项

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