Urban transportation networks, consisting of numerous links and nodes, experience traffic incidents such as accidents and road maintenance work. A typical consequence of incidents is congestion which results in long queues and causes high travel time variability. In order to combat the negative effects due to congestion, various mitigation strategies have been proposed and implemented in the United States and worldwide. The effectiveness of these congestion mitigations for incident conditions largely depends on the accuracy of information regarding conditions. Therefore, an efficient and accurate procedure to determine the link states, reflected by flows and density over time, is essential to incident management. This research project constructs a user equilibrium Dynamic Traffic Assignment (DTA) model using linear programming (LP) that incorporates the Cell Transmission Model (CTM) to evaluate the temporal variation of flow and density over links, which accurately reflect the link states of a transportation network. The proposed model adopts a scheme of bi-level optimization in which the upper level program determines the flows over the network while the lower level program (CTM) propagates flows according to widely-accepted traffic flow theory. Encapsulation of the CTM equips the model with the capability of accepting inputs of incidents like duration and capacity reduction. Moreover, the proposed bi-level model is capable of handling multiple- origin-destination (OD) pairs, which is a strength that most LP-based DTA models do not possess. By using this model, the temporal variation of flows over links can be readily evaluated and thus it can be used to predict the time-dependent link states. The results of numerical examples show that the flow pattern preserves the user equilibrium principle and satisfies the First-In-First-Out (FIFO) condition. The link-based encapsulation of CTM is able to temporally capture the queue between links and fully mimics the spillback within links.


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    Titel :

    Link State Relationships Under Incident Conditions: Using a CTM-Based Linear Programming Dynamic Traffic Assignment Model


    Beteiligte:
    W. Yin (Autor:in) / P. Murray-Tuite (Autor:in)

    Erscheinungsdatum :

    2010


    Format / Umfang :

    40 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch






    Dynamic Traffic Assignment Modeling for Incident Management

    Sisiopiku, Virginia P. | Online Contents | 2007


    Dynamic Traffic Assignment Modeling for Incident Management

    Sisiopiku, Virginia P. / Li, Xuping / Mouskos, Kyriacos C. et al. | Transportation Research Record | 2007