交通运输系统工程与信息

交通运输系统工程与信息 ›› 2011, Vol. 11 ›› Issue (2): 27-32.

• 智能交通系统与信息技术 • 上一篇    下一篇

面向诱导的交通状态概率预报技术

窦慧丽*1,2，王国华3，郭敏3   

  1. 1. 浙江交通职业技术学院 运输管理学院，杭州 311112；2. 同济大学 交通运输工程学院，上海 201804；3. 浙江省交通规划设计研究院 交通工程部，杭州 310006
  • 收稿日期:2010-12-09 修回日期:2011-02-11 出版日期:2011-04-28 发布日期:2011-05-10
  • 通讯作者: 窦慧丽
  • 作者简介:窦慧丽(1979-)，女，河南郏县人，讲师.
  • 基金资助:

    国家自然科学基金重点项目（70631002）; 国家863计划（2008AA11Z205）

Traffic Guidance Oriented Model of Traffic State Probability Forecast

DOU Hui-li1,2, WANG Guo-hua3, GUO Min3   

  1. 1. Institute of Transportation Management, Zhejiang Institute of Communications, Hangzhou 311112, China; 2. School of Transportation Engineering, Tongji University, Shanghai 201804, China;3. Department of Traffic Engineering, Zhejiang Provincial Institute of Communications Planning, Design and Research, Hangzhou 310006, China
  • Received:2010-12-09 Revised:2011-02-11 Online:2011-04-28 Published:2011-05-10
  • Contact: DOU Hui-li

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被引次数

7

摘要： 为了获取准确客观的交通流运行状态信息，满足交通诱导的需求，综合考虑城市道路中交通流演变的随机性和复杂性，以及实时交通状态判别本身具有的不确定性，通过分析交通状态和影响因素之间的映射关系，提出了一种交通状态概率预报的Logistic回归模型. 该模型借助Logistic回归方法探讨交通状态和交通参数之间的函数关系，并对下一时段交通状态及其发生的概率进行预报. 最后结合实际数据，进行了不同预报时长的分级交通状态的概率预报实验，独立样本检验结果表明，该模型预报准确率高，稳定性好.

关键词: 智能交通, 概率预报, Logistic回归, 交通状态, 交通诱导

Abstract: In order to obtain the accurate and objective traffic state information to meet the demand of traffic guidance, and in view of the stochastic property and complexity of the traffic flow evolution on the urban road and the uncertainty of traffic state discrimination, an algorithm of Logistic regression for traffic state probability forecast is put forward based on the analysis of the mapping relationships between traffic state and traffic flow parameters. The proposed algorithm explores the function relationships of traffic state and the influencing factors by means of Logistic regression and thus gives the probability prediction of the traffic state of the next time period. Finally, according to the proposed algorithm, the grading traffic state probability forecast tests of different time periods is carried out using the field traffic flow data. The results of independent sample test indicate that the model has a finer precision and stability.

Key words: intelligent transportation, probability forecast, Logistic regression, traffic state, traffic guidance

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