To quantitatively investigate the temporal and spatial impact of traffic incidents on the traffic flow of freeways, this paper proposes a novel method to model the impact of incidents based on the Rule engine and Knowledge Graph. Firstly, the speed change ratio selected as the index is used to characterize the effect of incidents. Through analyzing freeway traffic flow as a result of the traffic incidents, potentially influencing features are exploited to classify incidents. Subsequently, we model the impact of each incident class and express the rules of the impacted area with production rules to establish the rule base. Secondly, based on traffic information in a time-varying road network environment, a traffic incident knowledge model is constructed with an ontology model. Consequently, the rules of impact are matched by a forward rule engine with production rules. In the case analysis, the research results show that we model the impact as a quantitative time-varying spatial span. It builds a causal mechanism of the road network after an incident and provides directions for making evacuation plans to eliminate potential congestion.


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    Titel :

    Analysis of Spatiotemporal Impact of Traffic Incidents on Road Networks


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zhang, Zhenyuan (Herausgeber:in) / Zhang, Hui (Autor:in) / Zhang, Weibin (Autor:in) / Li, Jun (Autor:in) / Li, Xiying (Autor:in)

    Kongress:

    International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021



    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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