Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.


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    Titel :

    Real-Time Traffic Network State Prediction for Proactive Traffic Management


    Untertitel :

    Simulation Experiments and Sensitivity Analysis


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:


    Erscheinungsdatum :

    04.04.2019




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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