The invention provides an urban traffic abnormality identification method based on a complex network theory. The method comprises the following steps: step 1, constructing an urban traffic network based on traffic data; step 2, carrying out feature extraction and screening based on a complex network theory; step 3, carrying out the abnormality recognition and prediction of a traffic system; and step 4, evaluating and verifying a model. According to the invention, the scientific and reliable technical support and theoretical support can be provided for the recognition and prediction of the urban traffic congestion abnormality based on the complex network theory and a machine learning method. Therefore, the congestion abnormality of the urban traffic system can be efficiently and accuratelyidentified and predicted, and the method has important significance in ensuring the healthy and stable operation of the urban traffic system and improving the reliability of the urban traffic system;and the method is scientific and good in manufacturability and has the great application and popularization value.

    本发明提供一种基于复杂网络理论的城市交通异常识别方法,其步骤如下:步骤1,基于交通数据构建城市交通网络;步骤2,基于复杂网络理论的特征提取及筛选;步骤3,交通系统的异常识别及预测;步骤4,模型评价及验证;通过以上步骤,本发明基于复杂网络理论及机器学习方法,为城市交通拥堵异常的识别及预测提供了科学可靠的技术支持和理论支撑;本发明提出的技术方案能够高效准确地对城市交通系统的拥堵异常进行识别和预测,对保证城市交通系统健康平稳的运转和提高城市交通系统的可靠性具有重要意义;本发明所述方法科学,工艺性好,具有广阔推广应用价值。


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    Title :

    Urban traffic abnormality identification method based on complex network theory


    Additional title:

    一种基于复杂网络理论的城市交通异常识别方法


    Contributors:
    LI DAQING (author) / ZHENG CAN (author)

    Publication date :

    2020-04-28


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS





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