The invention relates to the technical field of traffic, and discloses a road condition forecasting and early warning method and system based on intelligent traffic, and the method comprises the steps: constructing a road feature network comprising road environment features, road historical traffic features and feature similarity values between roads, and an adaptive prediction model; and learning the characteristics of each road of the road characteristic network through an adaptive prediction model, predicting the development trend of the abnormal traffic event based on the identified abnormal traffic event, the real-time road information of the target road, and the road characteristics of the target road and the similar road, and dynamically adjusting the traffic dispersion strategy according to the prediction result. According to the invention, a traffic management department can make a more reasonable traffic dispersion strategy according to historical experience of similar roads, the response efficiency of the whole traffic system is improved, and the accident endangering range is effectively reduced.

    本发明涉及交通技术领域,公开了一种基于智能交通的道路状况预告预警方法和系统,通过构建包括道路环境特征、道路历史交通特征以及各道路之间的特征相似值的道路特征网和自适应预测模型,通过自适应预测模型学习道路特征网各道路的特征,并基于识别出的异常交通事件、目标道路的实时道路信息、目标道路和相似道路的道路特征预测异常交通事件的发展趋势,根据预测结果,动态调整交通疏导策略,本发明使交通管理部门能够根据类似道路的历史经验制定更加合理的交通疏导策略,提升整个交通系统的响应效率,有效缩小事故危及范围。


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    Title :

    Road condition forecasting and early warning method and system based on intelligent traffic


    Additional title:

    一种基于智能交通的道路状况预告预警方法和系统


    Contributors:
    WANG LIXIANG (author) / WANG MINGYONG (author) / HUANG RUIHUA (author) / WANG WENXING (author) / XIE JIANXING (author)

    Publication date :

    2024-11-29


    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 / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V



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