Bilevel multiobjective control enhances arterial performance via spatiotemporal optimization of presignalized intersections

To mitigate urban congestion and promote sustainable mobility, the Pre-signal system has been widely adopted to exploit spatio-temporal resources at saturated intersections. However, existing studies predominantly focus on isolated intersection optimization. The application of pre-signals in arterial coordination remains challenging because the “secondary queuing” phenomenon in the sorting area disrupts the platoon dispersion characteristics relied upon by traditional green wave strategies. To address this gap, this paper proposes a hierarchical control architecture integrating “intra-intersection main-pre signal synergy” and “inter-intersection arterial coordination.” First, the spatio-temporal evolution mechanism of traffic flow under pre-signal conditions is analyzed. Subsequently, a Multi-Objective Optimization Problem (MOP) model targeting maximized throughput, minimized average delay, and reduced queue lengths is formulated. A Decomposition-Based Multi-Objective Evolutionary Algorithm (MOEA/D) is explicitly designed to solve this complex problem involving conflicting objectives. VISSIM-based simulation results demonstrate that the proposed method achieves a superior balance between operational efficiency and environmental performance. Compared with single-objective optimization, the proposed framework increases throughput by approximately 12% and reduces average vehicle delay by 6%. Furthermore, compared with uncoordinated control, the maximum queue length is reduced by 6–15%, while throughput is enhanced by 18–39%. This study provides a robust implementation framework for alleviating spillback risks and enhancing the sustainability of complex urban arterials.