With the rapid growth of car ownership, the scale and the density of the road network are also increasing, but it is followed by large-scale, regional traffic congestion, and frequent traffic accidents. Traffic situation risk situational awareness is of great significance to reduce regional traffic operation risks and improve the overall efficiency of traffic operations. Therefore, this paper selects the influencing factors of traffic operation situation in road risk, traffic risk, and environmental risk, and determines the representative multi-class indicators of the three categories of indicators as the key influencing factors of traffic operation situation. In order to assess the risk of key influencing factors, the combination of random forest, FAHP, and fine Kinney was used to hierarchical analysis of traffic situation. At the same time, the traffic situation prediction model is established based on HMM to verify the accuracy and error of the prediction results. Taking Xi’an ring expressway as an example, the constructed model is used to predict the traffic situation of the road network and evaluate the prediction results. After comparing the accuracy and error of HMM, autoregressive moving average model, and gray Markov model, the HMM prediction model proposed in the paper can’t only predict the situation value of the road network traffic situation as a whole, but also has higher accuracy and less error.


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    Titel :

    HMM-Based Traffic Situation Assessment and Prediction Method


    Beteiligte:
    Luo, Zhongbin (Autor:in) / Shi, Heng (Autor:in) / Liu, Weiwei (Autor:in) / Jin, Yuanyuan (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 4205-4217


    Erscheinungsdatum :

    09.12.2020




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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