Highlights A real-time traffic network management system with decision support capabilities. Simulation-based real-time traffic network state estimation and prediction. A closed-loop rolling horizon framework to generate proactive traffic management schemes. Consistency of the schemes with the network congestion and drivers’ route choice behavior. Comprehensive traffic management schemes considering all possible control strategies.

    Abstract This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.


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    Title :

    Real-time traffic network state estimation and prediction with decision support capabilities: Application to integrated corridor management




    Publication date :

    2016-10-24


    Size :

    19 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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