Treffer: Traffic responsive urban predictive control for signal splitting in large-scale congested urban road networks.

AbstractThis article introduces a novel centralized model predictive control (MPC) framework for the time splitting of traffic lights in large-scale urban road networks, based on a significantly improved Store-and-Forward (SaF) traffic flow model. The conventional SaF model relies on a restrictive assumption of sufficient demand and free space in the links, which limits its applicability and accuracy. In this work, by disregarding this assumption, a more general and realistic time-varying model is obtained. To address the increased complexity, we propose a groundbreaking mapping that transforms the time-varying model back into a linear time-invariant one. This mapping is supported by a necessary and sufficient condition, along with rigorous proofs, ensuring its applicability to any traffic network, regardless of size or topology. Ignoring the restrictive assumption not only enhances the model’s accuracy but also eliminates the need for an additional constraint in the optimization problem, enabling more efficient use of available green time resources. Building on this improvement, we formulate a hybrid traffic-responsive and fixed-time demand-based strategy within the MPC framework. This strategy combines the strengths of demand-based and traffic-responsive strategies, resulting in a quadratic programming (QP) problem that is computationally efficient and suitable for real-time implementation. The proposed framework is extensively evaluated using MATLAB and the SUMO traffic simulator. Simulation results demonstrate that the modified SaF model achieves significantly higher prediction accuracy compared to the conventional model. Furthermore, the proposed control framework outperforms benchmark methods, including demand-based control, conventional MPC, and the well-known traffic-responsive urban control (TUC) strategy. [ABSTRACT FROM AUTHOR]

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