计算机应用 ›› 2012, Vol. 32 ›› Issue (04): 905-909.DOI: 10.3724/SP.J.1087.2012.00905

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基于精确传感网络的智能交通系统交通流模型

王涛,李志蜀   

  1. 四川大学 计算机学院,成都 610064
  • 收稿日期:2011-11-29 发布日期:2012-04-20 出版日期:2012-04-01
  • 通讯作者: 王涛
  • 作者简介:王涛(1979-),男,山东淄博人,博士研究生,主要研究方向:智能交通系统;
    李志蜀(1946-),男,重庆人,教授,博士生导师,主要研究方向:计算机网络智能控制。
  • 基金资助:
    国家863计划项目;国家自然科学基金资助项目;四川省应用基础研究资助项目

Traffic flow model in intelligent transport system based on precise sensor network

WANG Tao,LI Zhi-shu   

  1. School of Computer Science, Sichuan University, Chengdu Sichuan 610064, China
  • Received:2011-11-29 Online:2012-04-20 Published:2012-04-01
  • Contact: WANG Tao

摘要: 首先介绍了基于精确传感网络的智能交通系统(ITS)相对于传统交通流传感器网络的优势;然后基于组合预测理论对这类网络的基本交通流模型进行了研究,在模型中引入了更加精确的交通流物理量,包括旅行时间、路段上游及下游的分类交通流量等变量,使所建立模型的可解释信息量更加丰富和易懂,该模型算法为动态算法。交通实测数据实验证明模型的拟合精度较高,拟合值与真值的平均绝对误差值控制在9s以内,平均相对误差值控制在5%以内,综合各个时段来看,预测的准确度都在90%以上。最后总结了基于精确传感器网络的智能交通系统在实际交通应用中的重大价值。

关键词: 精确传感网络, 智能交通系统, 旅行时间, 分类交通流量, 交通流模型

Abstract: The advantage of precise sensor network compared to the traditional sensor network was introduced firstly. Then the traffic flow model of precise sensor network was built. To improve the interpretability of the model, the precise license plate identification data were used, and the variables such as the space travel time and the classified traffic flow were introduced. The established model was dynamic in essence. The experimental results show that the fitting accuracy is higher and the mean absolute error between fitted and standard value is less than 9 seconds, and the mean relative error is less than 5%. The model has a high degree of accuracy above 90%. Finally, the great value of precise sensor network in real traffic environment was summarized.

Key words: precise sensor network, Intellignet Transport System (ITS), travel time, classified traffic flow, traffic flow model