首页> 外文期刊>European transport research review >A pattern matching approach to speed forecasting of traffic networks
【24h】

A pattern matching approach to speed forecasting of traffic networks

机译:一种模式匹配的交通网络速度预测方法

获取原文
           

摘要

Abstract Objective Unlike other existing traffic data collection techniques, probe vehicles, or floating cars traveling on a road network, have appeared as a complementary solution for increasing coverage areas without requiring expensive infrastructure investments. When organized in a fleet with communication capabilities and exchange of information with a central data system, they give rise to a Floating-Car Data (FCD) system. The purpose of this paper is to present a model for short-term traffic speed forecasting based on an operating FCD system, developed and operated in Italy, delivering real-time traffic speed information throughout the Italian motorway network and along some important arterial streets located in major Italian metropolitan areas. Design Specifically, a database covering the whole period ranging from April 2008 to October 2011 is available for Rome Ring Road, a toll-free motorway that encircles Rome (Italy), and the developed case study pertains to a portion of its available speed data. Method A Pattern Matching method of prediction will be detailed, which reports interesting properties in terms of forecast accuracy; the method tries to identify, in the past data history, patterns neighboring (in a proper sense) the current one, which describes the actual traffic load, and produces forecasts supposing that the current situation will evolve in a similar way.
机译:摘要目的与其他现有的交通数据收集技术不同,探测车或在道​​路网络上行驶的浮动车已成为一种补充解决方案,无需增加昂贵的基础设施投资即可扩大覆盖范围。当以具有通信功能并与中央数据系统交换信息的车队进行组织时,它们就产生了浮动车数据(FCD)系统。本文的目的是提出一个基于运行中的FCD系统的短期交通速度预测模型,该系统在意大利开发和运营,可在整个意大利高速公路网络以及位于意大利的一些重要干道上提供实时交通速度信息。意大利主要城市地区。设计具体而言,可使用涵盖罗马(意大利)的免费高速公路罗马环路的2008年4月至2011年10月整个时期的数据库,并且已开发的案例研究涉及其部分可用速度数据。方法将详细介绍一种预测的模式匹配方法,该方法报告有关预测准确性的有趣属性;该方法尝试在过去的数据历史中识别(正确意义上)与当前模式相邻的模式,该模式描述了实际的流量负载,并假设当前情况将以类似方式演变,从而产生预测。

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