Highlights We studies cooperative decision-making for mixed traffic at ramp-merging sections. Cooperative and non-cooperative microscopic decisions are explicitly considered. The problem is solved using a bi-level dynamic programming-based approach. The proposed CDMMT Ramp Merging method guarantees system-efficient results.

    Abstract The rapid conceptual development and commercialization of connected automated vehicle (CAV) has led to the problem of mixed traffic, i.e., traffic mixed with CAVs and conventional human-operated vehicles (HVs). The paper studies cooperative decision-making for mixed traffic (CDMMT). Using discrete optimization, a CDMMT mechanism is developed to facilitate ramp merging, and to properly capture the cooperative and non-cooperative behaviors in mixed traffic. The CDMMT mechanism can be described as a bi-level optimization program in which state-constrained optimal control-based trajectory design problems are imbedded in a sequencing problem. A bi-level dynamic programming-based solution approach is developed to efficiently solve the problem. The proposed modeling mechanism and solution approach are generic to deterministic decisions and can guarantee system-efficient solutions. A micro-simulation environment is built for model validation and analysis of mixed traffic. The results show that compared to the scenario with 100% HVs, ramp-merging can be smoother in mixed traffic environment. At high CAV penetration, the section throughput increases about 18%. With the proposed CDMMT mechanism, traffic throughput can be further increased by 10–15%. The proposed methods form the basis of traffic analysis and cooperative control at ramp-merging sections under mixed traffic environment.


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    Titel :

    Cooperative decision-making for mixed traffic: A ramp merging example


    Beteiligte:
    Sun, Zhanbo (Autor:in) / Huang, Tianyu (Autor:in) / Zhang, Peitong (Autor:in)


    Erscheinungsdatum :

    2020-08-16




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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