交通运输系统工程与信息

交通运输系统工程与信息 ›› 2013, Vol. 13 ›› Issue (3): 121-125.

• 系统工程理论与方法 • 上一篇    下一篇

大规模交通流预测方法研究

孙占全*，刘威，朱效民   

  1. 山东省计算中心 山东省计算机网络重点实验室，济南 250014
  • 收稿日期:2013-01-09 修回日期:2013-02-26 出版日期:2013-06-25 发布日期:2013-07-02
  • 作者简介:孙占全（1977-），男，黑龙江哈尔滨人，副研究员，博士.
  • 基金资助:

    国家自然科学青年基金项目（61004115）；国家科技支持计划项目（2012BAH09B03）.

Traffic Flow Forecasting Based on Large Scale Traffic Flow Data

SUN Zhan-quan, LIU Wei, ZHU Xiao-min   

  1. Shandong Computer Science Center, Shandong Provincial Key Laboratory of Computer Network, Jinan 250014, China
  • Received:2013-01-09 Revised:2013-02-26 Online:2013-06-25 Published:2013-07-02

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被引次数

5

摘要：

随着交通信息化的快速发展，可供分析的交通流数据量越来越大，如何利用大规模交通流数据进行交通预测分析是智能交通的重要研究内容.为解决大规模交通流数据预测问题，本文提出了一种基于分层抽样与k均值聚类相结合的抽样方法，并与基于序贯最小优化方法的支持向量机结合，进行大规模交通流预测.实例分析结果表明，本文提出的聚类方法比现有抽样方法的抽样质量有所提高，基于序贯最小优化方法的支持向量机可有效提高交通流预测的精度.因此，本文提出的方法对于大规模交通流预测是有效的.

关键词: 智能交通, 拥挤判别, 抽样, k均值聚类

Abstract:

With the development of traffic informatization, increasing amount of traffic data can be collected. How to make most of the traffic data to forecast traffic flow is a crucial work of the intelligent transportation systems (ITS). To resolve this problem, this paper proposes a sampling method based on the combination of stratified sampling method and kmeans clustering. The support vector machine (SVM) based on sequence optimization method is used to forecast traffic flow parameters. The analysis results show that the sampling quality based on the proposed sampling method is reformed. The forecasting precision based on the SVM also gets improved. It proves that the method is efficient to solve largescale traffic forecasting problems.

Key words: intelligent transportation system, traffic congestion identification, sampling, k-means clustering

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